A3 problem analysis
Comprehensive A3 one-page problem analysis with root cause and action plan
Explore the method : A3 problem analysisA decision to make. A product to build. A brief to write.
Start with the work in front of you.
1149 skills to explore
Comprehensive A3 one-page problem analysis with root cause and action plan
Explore the method : A3 problem analysisWhen the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
Explore the method : Ab TestingStop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
Explore the method : Academy GuideWhole site or product — a full web accessibility (a11y) audit against WCAG 2.2, following the WCAG-EM methodology. Defines scope, samples representative pages and flows, runs the automated tier (`accessibility-scan`) and the hands-on manual tier (`accessibility-inspect`), and produces one conformance report. Grades each finding by severity and evidence basis, and states per-criterion conformance as pass, fail, or undetermined (needs a human). Use it for 'audit my site for accessibility', 'is this product accessible', 'a11y audit', 'WCAG or Section 508 conformance report', or any multi-page assessment. Assesses; does not fix (use `accessibility-fix`) or diff (use `accessibility-diff`). For a single page use `accessibility-scan`; for hands-on keyboard and screen-reader checks use `accessibility-inspect`.
Explore the method : Accessibility auditRegression check — diff a live page's web accessibility (a11y) violations against a baseline. By default it compares your uncommitted changes (stash-based); pass `--branch [<name>]` to compare against a branch. Reports the new WCAG violations introduced, the ones fixed, and the count of pre-existing ones. Use it for 'did my change break accessibility', 'what a11y issues did this PR add', or as a CI gate. For a full scan of one page use `accessibility-scan`; for a whole site use `accessibility-audit`.
Explore the method : Accessibility regression checkRemediation only — repair web accessibility (a11y) violations against WCAG 2.2 with a baseline, edit, and verify loop. Takes a target (URL, files, directory) or a findings worklist from `accessibility-scan`/`accessibility-inspect`/`accessibility-audit`, applies mechanical fixes as given, leaves TODOs for visual or contextual judgment, and verifies by re-running the baseline check. It only fixes. To find issues use `accessibility-scan` (one page, automated), `accessibility-inspect` (one page, manual), or `accessibility-audit` (whole site, WCAG-EM); to check for regressions use `accessibility-diff`. Use it for 'fix the a11y issues in X', 'make this accessible', 'add missing alt text and labels', 'apply these accessibility fixes', 'remediate these violations'.
Explore the method : Accessibility repairMake data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.
Explore the method : Accessible communication reviewGenerate, iterate, audit, and scale paid ad creative: headlines, descriptions, primary text, responsive-search-ad copy, social ads, bulk variations, creative-diversity checks, fatigue diagnosis, and testing plans. Use whenever the user asks for ad creative, ad-copy variations, platform ad text, bulk copy, creative testing, or creative-performance optimization.
Explore the method : Ad copywritingDesign or improve an AI agent runtime in application code: action spaces, tool contracts, observations, context, recovery, rate limits, and bounded execution. Use when building code that calls models, loops over tools, or orchestrates child agents; not for operating the current host's built-in delegation features.
Explore the method : Agent Harness ConstructionAgent instruction configuration. Configure GitHub Copilot with custom instructions. Use when setting up .github/copilot-instructions.md, customizing Copilot behavior, or creating repository-specific AI guidance. Triggers on Copilot instructions, copilot-instructions.md, GitHub Copilot config. Detect installed AI coding agents and append honest-feedback rules to their instruction files. Scans for config files (CLAUDE.md, copilot-instructions.md, .cursorrules, etc.), adapts output format per agent, and appends directives that disable sycophancy and enable constructive pushback. Use when setting up honest feedback, disabling people-pleasing, or enabling objective criticism. Triggers on honest agent, objective feedback, no sycophancy, honest criticism, contradict me, challenge assumptions, honest mode, brutal.
Explore the method : Agent instruction configurationDesign AI-first interfaces that build ongoing relationships through memory, trust evolution, and collaborative planning, not just isolated screen interactions
Explore the method : Agentic relationship designAI image production. Generate images, videos, and audio with fal.ai serverless AI. Use when building AI image generation, video generation, image editing, or real-time AI features. Triggers on fal.ai, fal, AI image generation, Flux, SDXL, real-time AI, serverless AI. Master prompt-engineer for photoreal, artifact-free AI still images on ANY tool (Reve, Midjourney, Flux, GPT-image, Imagen, Nano Banana, Stable Diffusion). Builds prompts that hit National-Geographic-grade realism — true skin/fur texture (no plastic), correct anatomy/hands/faces, physically coherent light/shadows/reflections, clean legible text — while engineering deliberate visual IMPACT (color contrast, composition, awe/adrenaline, a striking point of view). Runs a gated process: lock the point-of-view and build the 8-block Capture Stack BEFORE generating, then run a.
Explore the method : AI image productionRegression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.
Explore the method : AI regression testingWhen the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' 'WebMCP,' 'do listicles still work for AI,' 'ChatGPT stopped citing comparison pages,' 'how do LLMs see our brand,' 'LinkedIn for AEO,' or 'AI citation format shift.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.
Explore the method : AI SeoWhen the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For choosing attribution models, comparing multi-touch/MMM/incrementality, or reconciling conflicting numbers across tools, see attribution. For A/B test measurement, see ab-testing.
Explore the method : AnalyticsBuild data visualization and analytics dashboards. Use when creating charts, KPI displays, metrics dashboards, or data visualization components. Triggers on analytics, dashboard, charts, metrics, KPI, data visualization, Recharts.
Explore the method : Analytics dashboard developmentAssess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.
Explore the method : Analyze Data QualityREST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.
Explore the method : API designDevelop a defensible outline for a substantive article, newsletter essay, advisory, white paper, or long-form point of view. Use when the user has a topic, rough ideas, research, or a desired claim and needs the argument, evidence plan, audience, and section sequence settled before drafting.
Explore the method : Article outliningDraft or revise a substantive article, newsletter essay, advisory, white paper, or long-form point of view from an approved outline or supplied source material. Use when the user needs a clear, evidence-bounded draft for a named reader, not merely a short social post or a publishing action.
Explore the method : Article writingWhen the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve it.
Explore the method : AsoAfter you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.
Explore the method : Assumption reviewPrepare an evidence-backed brief on every named meeting participant, tying public professional facts to a user-supplied thesis, challenge, or agenda and suggesting honest conversation openers. Use when a user has an attendee roster and needs person-by-person research for an upcoming discussion. Use meeting-preparation for meeting history, decisions, commitments, or a general agenda; use Notion Meeting Intelligence when Notion-backed context, an agenda or pre-read, or related Notion work is primary; use research-synthesis for broad topic, company, or market research not organized around a roster.
Explore the method : Attendee researchWhen the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.
Explore the method : AttributionOne page, automated tier — run the web accessibility (a11y) rule engine against a live page and locate every violation it can detect mechanically. Pass a URL, a config target name (e.g. `accesslint:accessibility-scan dev`), or nothing to use the default target from `accesslint.config.json`. Ensures a debuggable Chrome, runs the @accesslint/core engine over CDP, and returns a worklist of live-DOM WCAG 2.2 violations, each grounded to its DOM selector and source `file:line`. Locates; doesn't edit. Use it for 'is this page accessible', 'check a11y on this URL', 'find contrast and alt-text issues', or to verify a UI change. For hands-on keyboard and screen-reader checks use `accessibility-inspect`; for a whole site or product use `accessibility-audit`; to diff against uncommitted changes or a branch use `accessibility-diff`.
Explore the method : Automated accessibility scanManage AWS accounts, organizations, IAM, and billing. Use when setting up AWS Organizations, managing IAM policies, controlling costs, or implementing multi-account strategies. Triggers on AWS Organizations, AWS IAM, AWS billing, Cost Explorer, SCPs, multi-account, AWS SSO, Identity Center.
Explore the method : Aws account governanceBackend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
Explore the method : Backend PatternsExpert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Backtest ExpertDesign banners for social media, ads, website heroes, creative assets, and print. Multiple art direction options with optional generated or supplied visuals. Actions: design, create, generate banner. Platforms: Facebook, Twitter/X, LinkedIn, YouTube, Instagram, Google Display, website hero, print. Styles: minimalist, gradient, bold typography, photo-based, illustrated, geometric, retro, glassmorphism, 3D, neon, duotone, editorial, collage.
Explore the method : Banner designBuild a new AI agent on AWS and deploy it easily, OR wrap and deploy an agent you already have, using the Amazon Bedrock AgentCore harness. Explains what an "agent harness" is (the runtime scaffolding around a model - agent loop, tool execution, memory, identity, observability), then gives two fully-working, verified paths - (A) scaffold + ship a new agent with the AgentCore CLI (create/dev/deploy/invoke), and (B) deploy a prepared agent (Strands, LangGraph, or custom) via the SDK wrapper or a FastAPI + Docker + ECR container. Every import and command is verified against official AWS/Strands docs. Use when the goal is to create, wrap, or deploy an agent on AWS AgentCore. Triggers on agent harness, AWS harness, AgentCore, AgentCore CLI, agentcore create, agentcore deploy, deploy agent on AWS, bring your own agent, Bedrock AgentCore.
Explore the method : Bedrock agentcore deploymentOperate customer billing workflows such as subscriptions, refunds, churn triage, billing-portal recovery, and plan analysis using connected billing tools like Stripe. Use when the user needs to help a customer, inspect subscription state, or manage revenue-impacting billing operations.
Explore the method : Billing case analysisCalculate plain coupon-bond or zero-coupon present value, yield to maturity, duration, and convexity using explicit supplied assumptions. The upstream fixed-income package is partial: planned curve construction, broader products, and DeFi lending integration are not implemented. Do not claim a full fixed-income research platform.
Explore the method : Bond PricingCreative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Explore the method : Brainstorm optionsYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Explore the method : BrainstormingCheck a Canva design against a brand kit and report where it diverges — off-palette colors, non-brand fonts, logo misuse, and off-tone copy. Read-only; makes no changes. Use when the user asks "is this on brand", "check this against our brand kit", "do a brand review", "does this match our brand guidelines", or "brand-check my design".
Explore the method : Brand consistency reviewBrand voice, visual identity, messaging frameworks, asset management, brand consistency. Activate for branded content, tone of voice, marketing assets, brand compliance, style guides.
Explore the method : Brand designApplies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Explore the method : Brand GuidelinesBuild a source-derived writing style profile from real posts, essays, launch notes, docs, or site copy, then reuse that profile across content, outreach, and social workflows. Use when the user wants voice consistency without generic AI writing tropes.
Explore the method : Brand voiceCreate a reusable, editable presentation template from approved brand evidence, with distinct master layouts, replacement guidance, accessibility checks, and an optional source-grounded example deck. Use when a team needs a repeatable PowerPoint or slide system rather than a single presentation.
Explore the method : Branded presentation templateGenerate Minervini-style breakout trade plans from VCP screener output with worst-case risk calculation, portfolio heat management, and Alpaca-compatible order templates (stop-limit bracket for pre-placement, limit bracket for post-confirmation). Use when user has VCP screener results and wants actionable trade plans with entry/stop/target levels and position sizing. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Breakout Trade PlannerVerify a deployed or runnable web experience with browser evidence: critical journeys, errors, responsive behavior, accessibility checks, and an explicit verdict. Use for exploratory browser QA and post-change interaction checks; use e2e-testing to create or maintain a persistent automated test suite.
Explore the method : Browser QABuild source-backed dashboards for monitoring performance, exploring drivers, or acting on product and business metrics. Use when the task needs a dashboard, scorecard, or monitoring view with clear metrics, filters, source definitions, and QA.
Explore the method : Build DashboardBuild polished analytical reports for executive, product, business, or technical audiences. Use when the task needs a durable answer-first narrative with evidence-backed findings, visuals or tables, caveats, and source context.
Explore the method : Build ReportUse when building bots.
Explore the method : Build Zoom BotUse when using Contact Center.
Explore the method : Build Zoom Contact Center AppUse when embedding meetings.
Explore the method : Build Zoom Meeting AppUse when using Meeting SDK.
Explore the method : Build Zoom Meeting SDK AppUse when building Phone.
Explore the method : Build Zoom Phone IntegrationUse when calling REST APIs.
Explore the method : Build Zoom Rest API AppUse when building Team Chat.
Explore the method : Build Zoom Team Chat AppUse when using Video SDK.
Explore the method : Build Zoom Video SDK AppUse when using Virtual Agent.
Explore the method : Build Zoom Virtual AgentBuild fast applications with Bun JavaScript runtime. Use when creating Bun projects, using Bun APIs, bundling, testing, or optimizing Node.js alternatives. Triggers on Bun, Bun runtime, bun.sh, bunx, Bun serve, Bun test, JavaScript runtime.
Explore the method : Bun developmentAnalyze product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next.
Explore the method : Business analysisBuild and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, or when another skill needs to maintain the domain model.
Explore the method : Business glossaryTranslate PRD intent, roadmap asks, or product discussions into an implementation-ready capability plan that exposes constraints, invariants, interfaces, and unresolved decisions before multi-service work starts. Use when the user needs an ECC-native PRD-to-SRS lane instead of vague planning prose.
Explore the method : Business requirementsBuild an evidence-backed campaign strategy from market, customer, and positioning research. Use when a user needs a go-to-market campaign, outbound or account-based motion, activation calendar, channel plan, or a practical plan for reaching a defined buyer.
Explore the method : Campaign strategyScreen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : CANSLIM ScreenerCheck a Canva design against a brand kit and report where it diverges — off-palette colors, non-brand fonts, logo misuse, and off-tone copy. Read-only; makes no changes. Use when the user asks "is this on brand", "check this against our brand kit", "do a brand review", "does this match our brand guidelines", or "brand-check my design".
Explore the method : Canva Brand CheckCreate on-brand Canva presentations from an outline or brief. Use when the user asks to create a branded presentation, make an on-brand deck, turn an outline into slides, or generate a presentation from a brief. Input can be text directly in the message, a Canva design ID, a reference to a Canva doc by name, or a Canva design link (e.g., https://www.canva.com/design/...).
Explore the method : Canva Branded PresentationBulk-create Canva designs from tabular data using a brand template with autofill fields, producing one design per row. Use when users say "bulk create designs from this CSV", "generate one design per row", "create a design for each product", "batch generate from a template", or "autofill a template from a spreadsheet". Accepts any tabular data source — uploaded files, pasted tables, JSON, or URLs.
Explore the method : Canva Bulk CreateRead a Canva design and return structured, actionable design feedback — visual hierarchy, copy/messaging, layout & spacing, consistency, readability, and accessibility. Read-only; makes no changes to the design. Use when the user asks to "review my design", "give me feedback on this", "critique my deck/poster/flyer", "how can I improve this design", or "what's wrong with this slide".
Explore the method : Canva Design FeedbackMake edits to an existing Canva design — change or fix text, replace/insert/delete images and videos, reformat text (size, weight, style, color, alignment, lists, line height), reposition or resize elements, and update the title. Use when the user wants to change, edit, update, fix, translate, replace, or reformat content in a specific Canva design. This is the safe edit engine that other Canva skills (e.g. implement-feedback) build on.
Explore the method : Canva Edit DesignImplement reviewer feedback on a Canva design. Reads all comment threads, synthesises what reviewers want, makes the clear-cut changes directly, and flags anything that needs a human decision. Use when the user asks to "implement feedback on my deck", "address comments on a design", "apply review feedback", "fix the comments on my presentation", or "implement the feedback".
Explore the method : Canva Implement FeedbackResize a Canva design into multiple social media formats (Facebook post, Facebook story, Instagram post, Instagram story, LinkedIn post). Use this skill when users want to resize Canva designs specifically for multiple social media platforms in one operation, rather than resizing to a single format manually.
Explore the method : Canva Resize For Social MediaTranslate all text in a Canva design to another language, creating a translated copy. Faster than manually copying and editing each text box in Canva's editor. Use when users say "translate my design to [language]", "make a Spanish/French/etc version", or "localize my Canva design".
Explore the method : Canva Translate DesignCreate beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Explore the method : Canvas DesignPlan and run a bounded capability-feasibility investigation when a question such as 'can AI do X?' needs several independent tests rather than a one-shot answer. Use for capability spikes, technical due diligence, production-readiness research, and ranked go/no-go recommendations. Produces a cited per-spike record and synthesis; it does not deploy a system, spend funds, or delete artifacts without separate authorization.
Explore the method : Capability feasibility planningCodified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.
Explore the method : Carrier performance reviewUse when building public-equity-investing catalyst calendars. Do not use for full event underwriting; use event-driven-analyzer.
Explore the method : Catalyst CalendarDevelop a reproducible trading strategy through the CBT framework: define a hypothesis, inspect data, configure and build a backtest, compare experiments, optimize with held-out data, and report results. Use for CBT commands or a coordinated strategy research/build workflow. Live bots and exchange deployments remain planning only.
Explore the method : CBT backtesting workflowDesign, build, revise, or QA quantitative charts and figures. Use when an analytical answer needs visual judgment, whether for an inline answer, report, dashboard, notebook, or artifact.
Explore the method : Chart planningPlan a trip, compare current flight and hotel options, and hand the traveler a verified booking link when a connected travel source can provide one. Use for flight or hotel discovery, trip planning, and checkout handoff. ChiefOS handles provider integrations; travelers never need API keys.
Explore the method : ChiefOS travel planning and bookingUse when choosing architecture.
Explore the method : Choose Zoom ApproachWhen the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers or wants to build systems to prevent it. For post-cancel win-back email sequences, see emails. For in-app upgrade paywalls, see paywalls.
Explore the method : Churn PreventionCinematic video landing pages. Build a premium cinematic landing page with mouse-scrub video hero and brand-driven narrative-arc sections. Use whenever the user provides a hero video plus a product / subject / brand and wants a landing page, promo site, product showcase, marketing page, or storytelling site. Works for any language (RTL or LTR — Hebrew, English, Arabic, Spanish, French, Japanese, etc.) and any subject (food, tech, animals, fashion, services, SaaS, wildlife campaigns, music releases, books, real estate). The signature effect is mouse-driven video scrubbing — the hero video lives across the entire page as a fixed backdrop, and moving the mouse left-right scrubs the video timeline so the subject responds to the cursor. Below the hero, 4-5 fully-opaque sections each carry their own brand identity (color, typography emphasis,.
Explore the method : Cinematic video landing pagesComprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
Explore the method : Citation reviewClarify requirements before implementing. Use when serious doubts arise.
Explore the method : Clarify requestBuild and deploy on Cloudflare's edge platform. Use when creating Workers, Pages, D1 databases, R2 storage, AI inference, or KV storage. Triggers on Cloudflare, Workers, Cloudflare Pages, D1, R2, KV, Cloudflare AI, Durable Objects, edge computing.
Explore the method : Cloudflare developmentWhen the user wants to find co-marketing partners, plan joint campaigns, or brainstorm partnership opportunities. Use when the user says 'co-marketing,' 'partner marketing,' 'joint campaign,' 'who should we partner with,' 'integration marketing,' 'cross-promotion,' 'collaborate with another company,' 'partnership ideas,' or 'co-brand.' For customer referral programs, see referrals. For launch-specific partnerships, see launch.
Explore the method : Co MarketingGuide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Explore the method : Coauthor documentShared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
Explore the method : Codebase designAnalyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.
Explore the method : Codebase onboardingApply cross-project coding discipline: surface material assumptions, choose the smallest sufficient design, make surgical changes, and define fresh verification before claiming success. Use when planning, implementing, refactoring, or reviewing code; use framework-specific methods for current APIs and domain patterns.
Explore the method : Coding StandardsWrite B2B cold emails and follow-up sequences that get replies. Use when the user wants to write cold outreach emails, prospecting emails, cold email campaigns, sales development emails, or SDR emails. Also use when the user mentions "cold outreach," "prospecting email," "outbound email," "email to leads," "reach out to prospects," "sales email," "follow-up email sequence," "nobody's replying to my emails," or "how do I write a cold email." Covers subject lines, opening lines, body copy, CTAs, personalization, and multi-touch follow-up sequences. Emails avoid AI tells like 'it's not X, it's Y' reveals and 'no X, no Y, no Z' lists. For warm/lifecycle email sequences, see emails. For sales collateral beyond emails, see sales-enablement.
Explore the method : Cold EmailPlanning skill that helps the developer pick the right Twilio messaging channel — SMS, MMS, RCS, or WhatsApp — for a given use case. Qualifies intent across content type, geography, use case (marketing / notifications / OTP / support), cost model, and brand presence. Use when the developer asks "which channel should I use", "SMS vs RCS vs WhatsApp", mentions a country or region, asks about branded messaging, rich content, or fallback — and proactively when the developer says "send SMS" but their use case (rich content, international reach, branded experience) would benefit from a different channel.
Explore the method : Communication channel planningOperate notifications as one ECC-native workflow across GitHub, Linear, desktop alerts, hooks, and connected communication surfaces. Use when the real problem is alert routing, deduplication, escalation, or inbox collapse.
Explore the method : Communication escalation policyBuild and leverage online communities to drive product growth and brand loyalty. Use when the user wants to create a community strategy, grow a Discord or Slack community, manage a forum or subreddit, build brand advocates, increase word-of-mouth, drive community-led growth, engage users post-signup, or turn customers into evangelists. Trigger phrases: \"build a community,\" \"community strategy,\" \"Discord community,\" \"Slack community,\" \"community-led growth,\" \"brand advocates,\" \"user community,\" \"forum strategy,\" \"community engagement,\" \"grow our community,\" \"ambassador program,\" \"community flywheel.\
Explore the method : Community MarketingUse when creating source-backed public issuer tearsheets. Do not use for private diligence, fund diligence, vendors, or market maps.
Explore the method : Company briefA set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
Explore the method : Company newsletterUse when creating source-backed public issuer tearsheets. Do not use for private diligence, fund diligence, vendors, or market maps.
Explore the method : Company TearsheetProduce Public Equity Investing comparable-company valuation in report or workbook mode. Use for peer selection, multiple analysis, valuation read-throughs, implied prices, comps dashboards, Excel or Sheets comps, refreshable peer tables, model updates, and comps workbook QA. Do not use for DCF-only, credit-security, or generic market commentary requests.
Explore the method : Comparative valuation reviewConvene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.
Explore the method : Compare optionsAssess competitive density, concentration, strategic groups, differentiation, and a falsifiable position for a defined product, service, or operator decision.
Explore the method : Competitive analysisWhen the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
Explore the method : Competitor ProfilingWhen the user wants to create competitor comparison or alternative pages for SEO and buyer-facing use. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'comparison page,' '[Product] vs [Product],' '[Product] alternative,' 'competitive landing pages,' 'how do we compare to X,' 'competitor teardown,' 'audit our competitor pages,' 'are our comparison pages out of date,' or 'competitive asset audit.' Use this for any content that positions your product against competitors. Covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. For auditing existing claims (not researching competitors from scratch, which is competitor-profiling; not technical SEO on these pages, which is seo-audit), use the asset audit here. For internal battle cards and sales-specific competitor docs, see sales-enablement.
Explore the method : CompetitorsProduce Public Equity Investing comparable-company valuation in report or workbook mode. Use for peer selection, multiple analysis, valuation read-throughs, implied prices, comps dashboards, Excel or Sheets comps, refreshable peer tables, model updates, and comps workbook QA. Do not use for DCF-only, credit-security, or generic market commentary requests.
Explore the method : Comps ValuationConverts a pretrained 2D CNN into a 3D CNN by recursively replacing Conv2d/BN2d/Pool2d layers and inflating kernel weights along the depth axis.
Explore the method : Computer vision: 2d to 3d model conversionSelect center slices from a 3D volume and zero-pad along depth when the scan has fewer slices than the target count
Explore the method : Computer vision: 3d center crop zero pad3D ResNet encoder extracts volumetric features, pools depth dimension, then feeds into a 2D UNet/FPN decoder for segmentation
Explore the method : Computer vision: 3d encoder 2d decoder segmentationApplies Mixup augmentation to 3D volumetric images and their segmentation masks, interpolating both inputs and loss targets.
Explore the method : Computer vision: 3d mixup augmentationTiles 3D volumes into overlapping patches for inference and averages overlapping regions to produce seamless predictions.
Explore the method : Computer vision: 3d patch sliding window inferenceApplies albumentations augmentations to object detection data while preserving bbox-label correspondence via BboxParams.
Explore the method : Computer vision: albumentations detection augmentationResample a 3D medical volume to a fixed network input shape using physical voxel spacing (dz, dy, dx), correcting the Z dimension by the dz/dy ratio so anisotropic CT scans (1mm in-plane, 5mm slice) end up anatomically isotropic before trilinear interpolation
Explore the method : Computer vision: anisotropic spacing trilinear resampleArcFace angular margin loss layer for learning discriminative embeddings — used in image retrieval, product matching, and face recognition
Explore the method : Computer vision: arcface metric learningShow-Attend-Tell decoder: Bahdanau additive attention over CNN spatial features driving a gated LSTMCell for autoregressive image-to-sequence generation.
Explore the method : Computer vision: attention lstm image captioningAll-vs-all contrastive loss comparing every pair in a batch (N^2 pairs) with margin and compactification regularizer
Explore the method : Computer vision: batch all contrastive lossWrap albumentations bbox transforms in a 10-attempt retry loop that re-rolls the augmentation when all boxes get cropped out, preventing empty-target samples from poisoning the detection loss with NaN gradients
Explore the method : Computer vision: bbox augmentation retry loopInterpolate missing bounding boxes across video frames using bidirectional pandas interpolation to maintain smooth tracking through occlusions
Explore the method : Computer vision: bbox interpolation temporal trackingCombines BCE-with-logits and soft Dice loss with configurable weights for binary and multilabel segmentation training.
Explore the method : Computer vision: bce dice combined lossReplace a pretrained ResNet's conv1 with a wider input conv to accept stacked BEV raster channels (semantic map + agent history) while keeping downstream weights
Explore the method : Computer vision: bev conv1 channel expansionTwo-stage CT classifier where a 2D CNN dumps per-slice features once, then a bidirectional GRU runs over the slice sequence to produce both per-slice predictions (TimeDistributed head) and an exam-level prediction (avg+max pooled head) — turns expensive 3D CNN training into cheap sequence modeling
Explore the method : Computer vision: bigru slice feature aggregatorCrops uninformative black or dark borders from images by deriving a binary mask from the grayscale channel and trimming rows/columns.
Explore the method : Computer vision: black border autocropDetect all-black DICOM/MRI slices (mean==0) and replace them by randomly sampling a non-black slice from the same series
Explore the method : Computer vision: black slice replacementFuse low-resolution multispectral bands with a high-resolution panchromatic band using the Brovey transform to produce sharp multi-band imagery
Explore the method : Computer vision: brovey pansharpeningSelect a fixed number of Z-slices centered around the volume midpoint for memory-efficient 2.5D input from 3D CT/MRI stacks
Explore the method : Computer vision: center z slice selectionScore each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
Explore the method : Computer vision: centroid distance anomaly scoreMemory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Explore the method : Computer vision: chunked csv image generatorCompute pairwise cosine similarity on GPU in fixed-size chunks to avoid OOM, transferring only threshold-passing results to CPU
Explore the method : Computer vision: chunked gpu similarity searchDynamically undersample majority class each epoch with per-class keep probabilities for stochastic balance
Explore the method : Computer vision: class balanced epoch undersamplingSample training patches from large images using per-class area-fraction thresholds to ensure each patch contains meaningful object coverage
Explore the method : Computer vision: class density threshold patch samplingManual stable diffusion inference loop with classifier-free guidance that interpolates between unconditional and conditional noise predictions for controllable image generation
Explore the method : Computer vision: classifier free guidance diffusionUses a binary classifier's probability to gate object detector outputs in three tiers: keep detections, append a no-finding box, or replace all detections.
Explore the method : Computer vision: classifier gated detection postprocessScores image aesthetic quality by passing L2-normalized CLIP ViT-L/14 embeddings through a trained MLP regressor head.
Explore the method : Computer vision: clip aesthetic scoringGenerate descriptive text prompts from images by combining BLIP captioning with CLIP cosine similarity against curated label banks for medium, movement, and flavor attributes
Explore the method : Computer vision: clip interrogator captioningStrips the global pool and FC head from a pretrained CNN to expose spatial feature maps (H x W x C) for attention-based decoding.
Explore the method : Computer vision: cnn encoder spatial feature mapFuse CNN image features with a small tabular MLP branch via concat before a final classifier, training both branches end-to-end
Explore the method : Computer vision: cnn metadata fusion headExtract per-frame CNN features then classify the temporal sequence with stacked GRU layers and a boolean mask for variable-length video inputs
Explore the method : Computer vision: cnn rnn video classificationConvert per-instance RLE or polygon annotations to COCO JSON format for seamless use with Detectron2 and MMDetection
Explore the method : Computer vision: coco annotation conversionMatch two unequal-length point sets by enumerating which points to drop from the longer set, searching over deletion combinations
Explore the method : Computer vision: combinatorial deletion point matchingPasses N tiles independently through a shared CNN backbone, concatenates their feature maps spatially, then pools for classification — a lightweight multi-instance learning approach.
Explore the method : Computer vision: concat tile feature poolingAssigns multiple labels per sample using a confidence threshold on sigmoid outputs with a fallback negative class.
Explore the method : Computer vision: confidence threshold multilabelExtracts object centers from 3D segmentation masks using connected-component labeling and centroid computation.
Explore the method : Computer vision: connected components centroid extractionStack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Explore the method : Computer vision: conv1d lstm stroke classifierStratify train/validation split by binned mask coverage percentage to ensure balanced foreground representation in segmentation tasks
Explore the method : Computer vision: coverage stratified splitDrop false-positive detections by requiring matched per-frame box counts across paired synchronized camera views (Endzone vs Sideline) — if both views see the same scene at the same instant, true events should appear in both
Explore the method : Computer vision: cross view count consistency filterDetect inverted CT slice ordering by comparing ImagePositionPatient[2] (the Z coordinate) of the first and last DICOM slice in a series, flipping the volume along axis 0 when needed so every patient ends up in canonical head→feet order
Explore the method : Computer vision: ct z stack orientation flipAdd a cumulative sum channel along the vertical axis to capture directional structural trends in grayscale images for segmentation
Explore the method : Computer vision: cumulative sum channelFuses 3D object detections from multiple models by clustering nearby predictions with DBSCAN and taking cluster centroids.
Explore the method : Computer vision: dbscan ensemble fusionRun DeepSort on per-frame detections then overwrite each track cluster's label with the most common mapped label across the track's lifetime
Explore the method : Computer vision: deepsort majority vote relabelCustom Detectron2 data mapper with photometric augmentations that properly transforms images, bounding boxes, and instance masks in sync
Explore the method : Computer vision: detectron2 custom data mapperPartition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Explore the method : Computer vision: deterministic hash partitioningDice coefficient loss for pixel-level segmentation that directly optimizes the overlap between predicted and ground-truth masks.
Explore the method : Computer vision: dice lossFix pixel overflow artifacts in 12-bit unsigned DICOM files where values wrap around at 4096
Explore the method : Computer vision: dicom 12bit pixel correctionNormalize DICOM pixel values using frequency-equalized histogram bins for globally consistent non-linear intensity mapping
Explore the method : Computer vision: dicom freq histogram normalizationConvert raw DICOM pixel arrays to Hounsfield Units using per-slice RescaleSlope/RescaleIntercept, with outside-scanner clamping
Explore the method : Computer vision: dicom hounsfield normalizationOrder DICOM slices into a coherent 3D volume by sorting on ImagePositionPatient[2] (the Z coordinate in patient space), with a filename-integer fallback for series whose tag is missing — never trust filename alphabetical order, never trust InstanceNumber
Explore the method : Computer vision: dicom ipp z slice sortDecodes MRI scan plane (axial, coronal, sagittal) from DICOM ImageOrientationPatient direction cosine vectors.
Explore the method : Computer vision: dicom orientation detectionRead DICOM X-ray files with VOI LUT transformation and MONOCHROME1 inversion for correct pixel intensity rendering
Explore the method : Computer vision: dicom voi lut preprocessingUse a soft macro-F1 loss `1 − mean(2·tp / (2·tp + fp + fn))` computed from raw sigmoid probabilities (no thresholding) as a direct training objective for multi-label classification, optionally combined with BCE — closes the gap between training surrogate and the F1 metric the leaderboard scores
Explore the method : Computer vision: differentiable soft f1 lossRecover (x, y, class) point labels from color-coded dot-annotation image pairs via absdiff + blackout masking + Laplacian-of-Gaussian blob detection + center-pixel RGB classification
Explore the method : Computer vision: dot annotation blob diff extractionReshape dual-view stacked channels into doubled batch dimension for shared backbone, then concatenate with tabular features for classification
Explore the method : Computer vision: dual view reshape forwardLoad EfficientDet pretrained on COCO with the original 90-class head, then swap in a fresh HeadNet with your own num_classes — keeps the BiFPN feature pyramid pretrained and only retrains the classification head, the canonical transfer-learning recipe for the effdet PyTorch port
Explore the method : Computer vision: efficientdet headnet swapTracks an Exponential Moving Average of model weights during training and evaluates both live and EMA models for more stable predictions.
Explore the method : Computer vision: ema model averagingGPU-accelerated k-NN regression on CLIP image embeddings using cosine distance and inverse-distance-power weighting to predict target embedding vectors
Explore the method : Computer vision: embedding knn regressionCollect test predictions each epoch via callback and combine with exponentially increasing weights favoring later epochs
Explore the method : Computer vision: epoch prediction averagingAggregate per-slice predictions into exam-level labels that satisfy a competition's mutual-exclusion hierarchy (positive vs negative vs indeterminate), using a top-down rule cascade — first decide the exam class, then conditionally rescale the dependent labels so the submission stays internally consistent
Explore the method : Computer vision: exam level label hierarchy aggregationPad variable-length per-slice sequences to a fixed batch length, carry a 0/1 mask alongside, and multiply per-slice BCE by the mask before reducing — gives correct per-exam loss with batched training and zero contamination from padding tokens
Explore the method : Computer vision: exam sequence padded mask lossModels multiple conditions with a single flat output layer of N_labels × N_classes logits, sliced into per-condition softmax at inference.
Explore the method : Computer vision: flat multicondition headAlpha-weighted focal loss that down-weights easy examples to focus training on hard, misclassified pixels in imbalanced segmentation tasks.
Explore the method : Computer vision: focal lossEncode motion and velocity by computing per-channel pixel differences between consecutive frames instead of stacking raw frames for RL visual observations
Explore the method : Computer vision: frame differencing temporal encodingAverage per-frame sigmoid predictions across sampled video frames to produce a stable video-level classification probability
Explore the method : Computer vision: frame prediction averagingUnfreezes backbone layers for fine-tuning while keeping BatchNorm layers frozen to preserve pretrained running statistics.
Explore the method : Computer vision: frozen batchnorm finetuningGenerates 3D segmentation training targets by placing Gaussian spheres at annotated point coordinates.
Explore the method : Computer vision: gaussian sphere target generationReplaces global average pooling with Generalized Mean (GeM) pooling, using a learnable or fixed exponent to emphasize high-activation regions.
Explore the method : Computer vision: gem poolingClean noisy numeric strings from generative model output by removing invalid characters, fixing malformed floats, and handling multiple decimal points
Explore the method : Computer vision: generative output numeric cleaningAccumulates gradients over multiple mini-batches before stepping the optimizer, simulating larger effective batch sizes.
Explore the method : Computer vision: gradient accumulationResolve overlapping instance masks by greedily assigning contested pixels to higher-confidence predictions using a running occupancy map
Explore the method : Computer vision: greedy mask overlap resolutionOverlay real hair PNGs (masked via threshold) onto dermoscopy images to simulate body-hair occlusion as a domain-specific augmentation
Explore the method : Computer vision: hair overlay augmentationComprehensive albumentations augmentation combining geometric, photometric, noise, blur, and cutout transforms for robust CV training.
Explore the method : Computer vision: heavy augmentation pipelineInverts whole slide image pixel values (1 - x) so white background becomes zero, enabling standard zero-padding and making tissue regions the active signal.
Explore the method : Computer vision: histopathology image inversionApply radiological windowing to HU images — clamp to center/width range for tissue-specific visualization (lung, bone, soft tissue)
Explore the method : Computer vision: hu windowingEvaluates 3D object detection by matching predicted and ground-truth coordinates via the Hungarian algorithm, then computing F-beta score.
Explore the method : Computer vision: hungarian matching detection evalGrid search binarization thresholds on validation predictions to find the cutoff that maximizes mean IoU
Explore the method : Computer vision: iou threshold sweepEvaluation scorer that merges predictions with GT per frame, takes top-IoU match per GT, and computes weighted accuracy with IoU threshold gate
Explore the method : Computer vision: iou weighted assignment metricResize images preserving aspect ratio then zero-pad to a square to avoid distortion artifacts in face crops or object detection inputs
Explore the method : Computer vision: isotropic resize with paddingResample 3D CT volumes to uniform voxel spacing using scipy zoom, normalizing physical dimensions across scanners
Explore the method : Computer vision: isotropic voxel resamplingUse albumentations keypoint_params to jointly augment BEV rasters and trajectory target points so the spatial transform stays consistent
Explore the method : Computer vision: keypoint aware raster augmentationAverage predictions from K independently trained fold models at inference time for variance reduction without stacking complexity
Explore the method : Computer vision: kfold model averagingExtract an image's dominant RGB color via k-means over pixel-color space and emit three dense features capturing the modal color of the subject
Explore the method : Computer vision: kmeans dominant color extractionKNN-based retrieval with grid-searched distance threshold to convert embedding neighbors into match predictions
Explore the method : Computer vision: knn distance threshold matchingUse linear assignment problem (LAP/lapjv) on a score matrix to select globally optimal hard-negative pairs for metric learning
Explore the method : Computer vision: lap hard negative miningScore image sharpness with the variance of the Laplacian (Pech-Pacheco) as a single scalar feature for downstream tabular models or as a hard blur filter
Explore the method : Computer vision: laplacian variance blur scoreDisable horizontal-flip augmentation (both train-time and TTA) when label columns encode left/right anatomy — flipping silently corrupts the targets because "Left ICA" must map to "Right ICA" after a flip, not stay as "Left ICA"
Explore the method : Computer vision: lateralized label flip tta disableTrainable nonlinear distance metric that transforms (v1-v2) and (v1-v2)^2 through a linear layer before computing squared norm
Explore the method : Computer vision: learned distance metricEvaluates image-to-sequence models using mean Levenshtein edit distance between predicted and ground-truth strings.
Explore the method : Computer vision: levenshtein distance metricSubtracts a Gaussian-blurred version of the image from itself to normalize local illumination and enhance fine structural details.
Explore the method : Computer vision: local contrast enhancementLovasz hinge loss that directly optimizes IoU for binary segmentation by computing a convex surrogate via sorted prediction errors and cumulative Jaccard gradients.
Explore the method : Computer vision: lovasz hinge lossCompute mean Average Precision by sweeping IoU thresholds from 0.5 to 0.95 on RLE-encoded instance masks using pycocotools
Explore the method : Computer vision: map iou precision sweepConvert binary segmentation masks to Shapely MultiPolygons using cv2 contour hierarchy to correctly handle interior holes, with Douglas-Peucker simplification
Explore the method : Computer vision: mask to polygon contour hierarchyInject scalar metadata (depth, position, clinical features) into U-Net bottleneck via RepeatVector and Reshape for metadata-aware segmentation
Explore the method : Computer vision: metadata injection bottleneckMask the corners of a dermoscopy image with a random-radius black circle to mimic the dark vignette of a dermatoscope field of view
Explore the method : Computer vision: microscope circular mask augCompress a 3D medical volume into a 3-channel 2D image by stacking the middle slice, the max-intensity projection across depth, and the per-pixel std across depth — a poor-man's volumetric encoding that lets any pretrained 3-channel 2D CNN ingest a whole series in a single forward pass
Explore the method : Computer vision: middle mip std volume projectionRemoves predicted segmentation masks below a per-class minimum pixel area threshold to eliminate small false positive regions at inference time.
Explore the method : Computer vision: min area mask filteringUses PyTorch AMP autocast and GradScaler for FP16 training, halving memory usage and speeding up training on modern GPUs.
Explore the method : Computer vision: mixed precision trainingCombines mixup augmentation (linear interpolation of image pairs and their labels) with label smoothing in a single training pipeline for regularization.
Explore the method : Computer vision: mixup label smoothingRoute each DICOM series to a per-modality window-center / window-width pair (CT/CTA/MRA/MRI) before normalization, so the same model can ingest mixed modalities without one modality's intensity range washing out the others
Explore the method : Computer vision: modality adaptive dicom windowingSegment lung regions from CT using HU thresholding, connected-component labeling, and morphological opening
Explore the method : Computer vision: morphological lung segmentationMerges overlapping same-class bounding boxes from multiple annotators into a consensus box using IoU-based matching and intersection.
Explore the method : Computer vision: multi annotator bbox consensusCombine EfficientNetB0..B6 into one Keras model with a shared image input and one sigmoid head per backbone, training all N models in a single fit() call on TPU
Explore the method : Computer vision: multi efficientnet shared inputGenerate count-regression training patches at a geometric pyramid of image scales (0.9^k) so one CNN handles within- and between-image object-size variation without explicit anchors
Explore the method : Computer vision: multi scale patch training pyramidStacks uniformly sampled slices from multiple MRI series (e.g., Sagittal T1, T2, Axial) into a single multi-channel tensor for one-pass inference.
Explore the method : Computer vision: multi series channel stackingStack multiple CT window settings (brain, subdural, bone) as separate RGB channels for CNN input
Explore the method : Computer vision: multi window channel stackingComposites multi-channel imagery (microscopy, satellite) into 3-channel RGB for pretrained CNN backbones.
Explore the method : Computer vision: multichannel to rgb adaptationComputes per-class ROC-AUC then macro-averages for multi-label classification evaluation and model selection.
Explore the method : Computer vision: multilabel auc evaluationFor multi-label classification, compute the per-class binary correlation matrix restricted to multi-label rows and the conditional class counts given a rare anchor class — reveals label groupings the model can exploit (shared classifier heads, hierarchical loss weighting, post-hoc consistency rules)
Explore the method : Computer vision: multilabel cooccurrence correlation edaOversample multi-label images by giving each image a duplication multiplier equal to the max per-class multiplier among its labels, so every rare class gets repetition without exploding common-class counts — the standard fix for long-tail multi-label distributions where SMOTE / per-row oversampling doesn't apply
Explore the method : Computer vision: multilabel rare class image oversamplingCombine match predictions from image embeddings, text similarity, and perceptual hash via set union for maximum recall
Explore the method : Computer vision: multimodal prediction unionUNet++ dense cross-depth skip connections that propagate deeper decoder features into all shallower decoder levels
Explore the method : Computer vision: nested unet dense skip connectionsAuto-detect whether a generated data series is numeric or categorical by measuring the fraction of digit characters in the concatenated values
Explore the method : Computer vision: numeric categorical auto detectionAssign an unknown/novel class when all nearest-neighbor distances exceed a tuned cutoff threshold for open-set recognition
Explore the method : Computer vision: open set distance cutoffUses Nelder-Mead optimization to find per-class decision thresholds that maximize Quadratic Weighted Kappa for regression-to-ordinal conversion.
Explore the method : Computer vision: optimized ordinal thresholdsEncodes ordinal classes as cumulative binary labels (class N activates labels 0..N), enabling sigmoid + BCE training for ordinal regression.
Explore the method : Computer vision: ordinal multilabel encodingUses pack_padded_sequence to exclude padding tokens from cross-entropy loss in variable-length sequence generation.
Explore the method : Computer vision: pack padded sequence lossCompute Euclidean distance between entity pairs from tracking data and filter out pairs beyond a threshold to reduce inference candidates
Explore the method : Computer vision: pairwise distance proximity filterDouble left-join on tracking data to create pairwise features (positions, velocities, distance) for both entities in an interaction pair
Explore the method : Computer vision: pairwise tracking feature mergeTile a large aerial image into fixed-size patches, accumulate per-class point-annotation counts into a grid tensor aligned with the tiles, and train a small CNN to regress per-class object counts per patch under MSE
Explore the method : Computer vision: patch grid count regressionStratifies CV folds at the patient level rather than image level, preventing data leakage when multiple images exist per patient.
Explore the method : Computer vision: patient level stratified kfoldApply class-specific confidence thresholds by inferring the dominant class per image and indexing into a per-class threshold array
Explore the method : Computer vision: per class score thresholdOptimize per-class decision thresholds for macro-F1 by replacing the non-differentiable hard threshold with a sigmoid-sharpened soft-F1 surrogate and fitting the per-class threshold vector via least-squares — averaged over multiple random validation splits to suppress overfitting on rare classes
Explore the method : Computer vision: per class soft f1 threshold fittingFit a per-label probability calibrator on out-of-fold scores using Platt scaling (logistic regression on raw scores) and fall back to isotonic regression for labels where the logistic doesn't converge — pickle the dict of fitted calibrators and apply at inference for a small but free leaderboard lift on multi-label classification
Explore the method : Computer vision: per label platt isotonic calibrationTrain separate models per imaging modality (FLAIR/T1w/T1wCE/T2w) and average their predictions for final ensemble
Explore the method : Computer vision: per modality ensemble averagingTrains one specialized model per imaging modality or series type, routing inputs by metadata at inference for modality-specific feature learning.
Explore the method : Computer vision: per modality separate modelSingle CNN backbone with one shallow Dense neck per organ and mixed sigmoid (binary) + softmax (multi-class severity) heads, trained with a dict of losses so each organ is calibrated independently while sharing visual features
Explore the method : Computer vision: per organ multihead sigmoid softmaxAggregate per-slice CNN predictions into a single patient-level injury score by mean-pooling across TTA copies first, then max-pooling across slices — the worst-slice wins, which matches the medical reality that one bad slice is enough to grade the patient
Explore the method : Computer vision: per patient slice maxpool aggregationNormalize high-dynamic-range satellite or medical imagery to [0,1] using per-channel percentile clipping to suppress outliers while preserving relative contrast
Explore the method : Computer vision: percentile contrast stretchGrid-searches the optimal classification threshold to maximize probabilistic F-beta score on validation predictions.
Explore the method : Computer vision: pfbeta threshold optimizationGroup near-duplicate images by perceptual hash (pHash) as a zero-cost baseline signal for product or image matching
Explore the method : Computer vision: phash duplicate groupingConvert (x, y, class) point annotations into a CNN classification training set by cropping fixed-size square patches centered on each point, using a numpy shape check to silently reject border-clipped crops
Explore the method : Computer vision: point centered fixed patch croppingStitch overlapping tile predictions into a full-resolution output by accumulating probabilities and dividing by per-pixel overlap counts
Explore the method : Computer vision: prediction map stitching averagingApply lower dropout in shallow/final U-Net layers and higher dropout in deep layers to preserve spatial detail while regularizing abstract features
Explore the method : Computer vision: progressive dropout unetCustom training callback that computes Quadratic Weighted Kappa on validation data each epoch and checkpoints the best model.
Explore the method : Computer vision: quadratic weighted kappa callbackSamples a fixed number of slices from variable-length CT/MRI stacks using quantile indexing to produce consistent input depth.
Explore the method : Computer vision: quantile slice samplingSets the binary classification threshold as a prediction quantile matching the expected positive prevalence rate, avoiding manual threshold tuning.
Explore the method : Computer vision: quantile threshold prevalence matchingExtract dense per-pixel motion fields between consecutive video frames using a pretrained RAFT model, producing an HxWx2 flow tensor that can be channel-stacked with RGB or used as a standalone motion feature for action / impact / event detection
Explore the method : Computer vision: raft optical flow extractionConverts a raster image to a size-bounded SVG via K-means color quantization, contour extraction, importance-ranked polygon assembly, and progressive simplification.
Explore the method : Computer vision: raster to svg polygon conversionLoad fluorescence microscopy images stored as 4 separate single-channel PNGs (red microtubules, green target protein, blue nucleus, yellow ER) into a single HxWx4 tensor, preserving the biological semantics of each channel rather than collapsing to RGB
Explore the method : Computer vision: rgby 4channel fluorescence loaderEncodes binary segmentation masks into compressed RLE format for efficient storage and submission.
Explore the method : Computer vision: rle mask encodingBrute-force a 2D rotation angle over a coarse grid to align field-coordinate points with image-plane detections when the camera angle is unknown
Explore the method : Computer vision: rotation search point registrationTest-time augmentation via 4 rotation angles (0/90/180/270), applying inverse rotation to each prediction before averaging
Explore the method : Computer vision: rotation tta segmentationUses Facebook's semi-weakly supervised ImageNet-pretrained models (trained on 940M unlabeled images) as CNN backbones for stronger transfer learning than standard supervised pretraining.
Explore the method : Computer vision: semi supervised pretrained backboneEncode text prompts into fixed-length dense vectors using SentenceTransformer for cosine-similarity evaluation in image-to-text retrieval tasks
Explore the method : Computer vision: sentence transformer target encoding2D CNN with asymmetric kernels — temporal convolutions (Nx1) then spectral convolutions (1xM) — to decouple time and feature extraction
Explore the method : Computer vision: separable temporal spectral cnnTrack dice score separately for positive (mask-present) and negative (empty-mask) images to avoid division distortion
Explore the method : Computer vision: separate pos neg dice trackingSiamese network head that compares two embeddings via element-wise multiply, add, abs-diff, and squared-diff features for verification tasks
Explore the method : Computer vision: siamese pairwise comparison headSigmoid-transformed normalized RMSE that maps error from [0,inf) to a bounded (0,1] similarity score using R2-score ratio
Explore the method : Computer vision: sigmoid normalized rmseResample a 3D medical volume to a fixed depth N (e.g. 32) and feed the N slices as input *channels* to a 2D CNN with `in_chans=N` instead of using a 3D conv backbone — gets the volumetric context for a fraction of the memory and lets you use any timm 2D pretrained model
Explore the method : Computer vision: slice as channel 2d cnnEncode the empirical per-position event rate as a soft prior over slice index by binning normalized slice location and looking up a precomputed weight vector — adds class-aware spatial context to a per-slice classifier without changing the model
Explore the method : Computer vision: slice location weighted priorPad 3D volumes with fewer slices than required by duplicating existing slices with slight brightness variation via convertScaleAbs
Explore the method : Computer vision: slice padding augmented duplicatesEncode game state as a Super Mini Map (SMM) with separate binary channels for players, ball, and ownership, bit-packed for efficient transfer in RL training
Explore the method : Computer vision: smm spatial observation encodingCyclic cosine annealing LR that produces M diverse snapshots from a single training run for free ensembling
Explore the method : Computer vision: snapshot cosine ensembleAggregate patch-level count-regression predictions into image-level totals by multiplying each prediction by a boolean mask (x * (x > tau)) instead of rounding, retaining fractional evidence above threshold while killing background noise
Explore the method : Computer vision: soft threshold count aggregationTurn sparse single-frame event labels (impacts, collisions, goals) into a usable detector training set by stamping the positive label onto a ±k-frame window around each event, then dropping any frame that contains no positives — gives the detector enough positive samples without changing the original annotation
Explore the method : Computer vision: sparse event temporal window expansionSplit large-image segmentation data into train/val by spatial rectangle regions with border buffer exclusion to prevent patch leakage
Explore the method : Computer vision: spatial rect train val splitCompute normalized spectral band ratios (NDWI, CCCI, NDVI) from multispectral imagery and threshold for binary segmentation of water, vegetation, or other targets
Explore the method : Computer vision: spectral index segmentationKeras callback implementing Stochastic Weight Averaging (SWA) — running mean of model weights over final training epochs
Explore the method : Computer vision: stochastic weight averagingStratifies cross-validation folds by output sequence length to ensure balanced length distributions across train/val splits.
Explore the method : Computer vision: stratified fold by sequence lengthNormalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Explore the method : Computer vision: stroke normalize simplify pipelineRender stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Explore the method : Computer vision: stroke temporal color renderingSerialize structured ground truth (chart data, table rows, form fields) as special-token-delimited sequences for generative vision-language model training
Explore the method : Computer vision: structured gt serialization tokensAdd random temporal offset to the center frame during training to augment temporal diversity in video-based models
Explore the method : Computer vision: temporal frame jitter augmentationPad batch sequence lengths to multiples of 8 for efficient tensor core utilization on GPUs, with -100 masking for label padding
Explore the method : Computer vision: tensor core aligned paddingCompiles a PyTorch model to a TensorRT FP16 engine via torch_tensorrt for 2-5x inference speedup, saved as reusable TorchScript.
Explore the method : Computer vision: tensorrt fp16 compilationApplies geometric and color augmentations at inference time and averages predictions to reduce variance.
Explore the method : Computer vision: test time augmentationAssembles N extracted tiles into a sqrt(N) x sqrt(N) mosaic image for single-forward-pass CNN inference on whole slide images.
Explore the method : Computer vision: tile grid mosaic assemblySelects the top-N most informative tiles from a gigapixel whole slide image by ranking on pixel intensity sum, keeping tiles with the most tissue content.
Explore the method : Computer vision: tissue content tile selectionPre-shard TFRecords into N files each balanced along 3 axes (patient, target, image-count), then KFold over file indices for leak-free triple-stratified folds
Explore the method : Computer vision: triple stratified shard foldsExtract volumetric features from 3D segmentation masks including scan/tumor pixel ratios, tumor percentage, and tumor centroid coordinates
Explore the method : Computer vision: tumor volume ratio featuresTversky loss with independent alpha/beta constants to separately penalize false positives and false negatives in imbalanced segmentation.
Explore the method : Computer vision: tversky lossTrain segmentation with BCE loss first for stable convergence, then fine-tune with Lovasz-hinge on raw logits for IoU-optimal predictions
Explore the method : Computer vision: two stage bce then lovaszChain a study-level classifier with an image-level detector, merging class probabilities and bounding boxes into a unified prediction
Explore the method : Computer vision: two stage classify then detectUse a lightweight slice-classifier to find the Z-range containing an organ in a CT volume, then crop and trilinear-resample that sub-volume into a fixed shape for a heavier 3D classifier — replaces "use the whole volume" with "use only the relevant slab" at a fraction of the FLOPs
Explore the method : Computer vision: two stage slice localization then organ cropEfficiently sample N evenly-spaced frames from a video using OpenCV grab/retrieve pattern with optional resize for batch face detection or classification
Explore the method : Computer vision: video frame sampling pipelineExtracts calibrated yes/no probabilities from a VQA model by masking all logits except yes/no token variants and renormalizing via softmax.
Explore the method : Computer vision: vqa yes no logit scoringCombines gradual learning rate warmup with cosine annealing decay for stable fine-tuning of pretrained models.
Explore the method : Computer vision: warmup cosine lr schedulePropagates image-level multi-labels to individual instances as weak supervision for instance-level training.
Explore the method : Computer vision: weak supervision label propagationBCE loss with per-class asymmetric positive/negative weights to match competition metrics or handle class imbalance in multilabel classification.
Explore the method : Computer vision: weighted bce lossEnsemble predictions from heterogeneous vision-language models by blending their output embeddings with fixed scalar weights in embedding space
Explore the method : Computer vision: weighted embedding blendingConvert bounding boxes between YOLO (normalized center), VOC (absolute corners), and COCO (absolute xywh) formats with image dimension scaling
Explore the method : Computer vision: yolo bbox format conversionCreate platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.
Explore the method : Content repurposingPlan content strategy, content pillars, topic clusters, editorial calendars, blog strategy, content marketing roadmaps, and what to write about. Use when the user asks for content ideas, blog topics, topic clusters, content planning, content pillars, content marketing, or does not know what content to create. For individual pieces or channel execution, name the appropriate specialist workflow.
Explore the method : Content strategyUnderstand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
Explore the method : Context briefingUse when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.
Explore the method : Continuous improvementSynthesize the three Jason Shapiro contrarian-pipeline verdicts (COT crowding, news-reaction failure, weekly price-action confirmation) into one actionable setup_status via a fail-closed precedence state machine. Pure, offline synthesis -- no network, no API keys, no computation beyond validation and precedence. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Contrarian Setup GateExpert UI/UX design guidance for unique, accessible interfaces. Use for visual decisions, colors, typography, layouts. Always ask before making design decisions. Use this skill when the user asks to build web components, pages, or applications.
Explore the method : Controlled UX designCompact the current conversation into a handoff document for another agent to pick up.
Explore the method : Conversation handoffBuild agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
Explore the method : Copilot sdk developmentWhen the user wants to edit, review, or improve existing marketing copy, or refresh outdated content. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' 'sharpen the messaging,' 'refresh this content,' 'update this page,' 'this content is outdated,' or 'content audit,' 'this sounds like AI,' 'AI slop,' 'de-slop this,' or 'make it sound human.' Use this when the user already has copy and wants it improved or refreshed rather than rewritten from scratch. Every edit removes AI tells such as 'it's not X, it's Y' reveals, 'no X, no Y, no Z' lists, and sentences that trail into extra comma clauses. For writing new copy, see copywriting.
Explore the method : Copy EditingWallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Copy Trading ResearchWrite and rewrite high-converting, plain-English marketing and sales copy for websites, brochures, landing pages, proposals, emails, ads, social posts, and client-facing collateral. Use this whenever the user asks for better copy, stronger positioning, clearer messaging, conversion copy, sales copy, homepage copy, brochure copy, landing page copy, offer copy, headlines, CTAs, or says copy is weak, generic, boring, confusing, too jargon-heavy, or not persuasive enough.
Explore the method : CopywritingCodified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.
Explore the method : Corrective action planningMulti-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Cost Basis EngineDetect crowded speculative positioning in CFTC futures markets (COT report analysis) to find contrarian setups using Jason Shapiro's methodology. Screens large-speculator ("non-commercial") net positioning across 65 futures markets (indices, rates, FX, metals, energy, crypto) via the FMP Commitment of Traders API, computes a 3-year and 26-week COT Index per market, and classifies extremes as CROWDED_LONG / CROWDED_SHORT. Use when the user asks about COT report analysis, crowded positioning, "who is trapped", speculative positioning extremes, contrarian futures setups, or wants to run Jason Shapiro-style analysis. This skill automates crowding DETECTION only (step 1 of 5) — it does not generate trade signals by itself. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : COT Contrarian DetectorCreate, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.
Explore the method : Create Data ContextCreate Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.
Explore the method : Create MCP serversGenerate brand-quality product images through Higgsfield product-photoshoot prompt enhancement on GPT Image 2 / gpt_image_2. Entry point for professional brand/product visuals. Use when: "product photo", "studio shot", "lifestyle image", "Pinterest pin", "hero/banner", "carousel", "ad creative", "Meta ads", "virtual try-on", "model wearing", "person holding product", "closeup with hands", "levitating/floating/splash product", "CGI/surreal product", "restyle", "seasonal/aesthetic variation", or any product, brand, or paid-social creative. Modes: product_shot, lifestyle_scene, closeup_product_with_person, moodboard_pin, hero_banner, social_carousel, ad_creative_pack, virtual_model_tryout, conceptual_product, restyle. Backend assembles the final prompt; never freehand it. NOT for: no-product text-to-image (use higgsfield-generate), branded avatar video (use higgsfield-generate Marketing Studio), marketplace listing cards (use higgsfield-marketplace-cards), Soul Character training (use higgsfield-soul-id).
Explore the method : Creative briefWhen the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For signup/registration flows, see signup. For post-signup activation, see onboarding. For popups/modals, see popups.
Explore the method : CroAnalyze cross-asset correlation, rolling stability, clustering, tail dependence, or correlation-based hedge sizing. Select the statistical or NinjaTrader futures-hedge method for the requested inputs. Distinguish correlation from cointegration and a hedge proposal from an executable order.
Explore the method : Cross Asset CorrelationRetrieve crypto prices, OHLCV, token/pool metadata, DeFi metrics, wallet balances, or parsed chain observations through a suitable available provider. Select Birdeye, CoinGecko, DeFiLlama, DexScreener, Helius, SolanaTracker, or Solana RPC based on the required evidence. Preserves provider-specific methods; signing and broadcasting remain outside this data-retrieval skill.
Explore the method : Crypto Market DataQuantifies crypto market regime health using free, keyless public data (CoinGecko + Binance funding). Generates a 0-100 composite score across 6 components (100 = risk-on) with a posture recommendation. No API key required. Use when user asks about crypto market conditions, whether it's alt season, BTC dominance, crypto risk-on vs risk-off, funding rates, or whether crypto exposure should be increased or reduced. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Crypto Regime AnalyzerExport trade history and tax calculations in formats compatible with Koinly, CoinTracker, CoinLedger, TokenTax, and IRS Form 8949. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Crypto Tax ExportMaintain a cited, living customer or account context record and append-only source ledger for technology, organization, processes, stakeholders, goals, conflicts, and open questions. Use only when a user explicitly asks to initialize, update, reconcile, report from, or preserve evidence in a named customer's durable context. Do not infer persistence from incidental conversation.
Explore the method : Customer context synthesisRun fast, source-grounded UX research on the highest-signal problems users are experiencing with a user-specified digital product. Use when the user asks to research user pain, UX friction, onboarding issues, docs/help problems, developer experience friction, support pain, product workflow issues, or current user complaints for a named product.
Explore the method : Customer feedback synthesisAudit or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, then reporting UX, design, and accessibility findings inline from that evidence. Use Figma only when the user explicitly asks for a board. Use when the user asks to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product experience.
Explore the method : Customer journey reviewExtract and govern verified customer or prospect language from supplied transcripts, notes, approved reviews, or other authorized material. Use when a user needs verbatim customer evidence, recurring pain themes, objection evidence, proposal proof, or content research. Keep entries internal until a specific external use is explicitly approved.
Explore the method : Customer language analysisWhen the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.
Explore the method : Customer ResearchPlanning skill for building customer service and support systems. Qualifies the developer's needs across the support ladder (self-service → AI agents → contact center), channel mix, and scale to recommend the right Twilio architecture. Handles both "build me a call center" and "add an IVR to my existing support line."
Explore the method : Customer support planningCoordinate a host's daily operating queue across relationship records, calendar, active work ledgers, customer context, review items, and growth workflows. Use when a user asks what needs attention today, wants a sales or follow-up queue, meeting preparation, post-call routing, reviewed draft creation, a weekly retrospective, or a recent-evidence reconciliation. This method coordinates host-owned workflows; it does not replace them.
Explore the method : Daily prioritizationBuild source-backed dashboards for monitoring performance, exploring drivers, or acting on product and business metrics. Use when the task needs a dashboard, scorecard, or monitoring view with clear metrics, filters, source definitions, and QA.
Explore the method : Dashboard briefValidate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Data Quality CheckerPerform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Explore the method : Data quality reviewDesign and implement parallax scrolling and scrollytelling data visualizations. Use when the user asks for parallax scrolling, scrollytelling, scroll-driven timelines, sticky graphics, Scrollama, ScrollTrigger, ScrollTimeline, view timelines, rich-media timelines, moviescrollers, scroll-scrubbed charts, staged narrative reveals, or interactive visual stories where scrolling changes a data visualization or media scene.
Explore the method : Data storytellingMeta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Explore the method : Data visualization critiqueDatabase migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Kysely, Django, TypeORM, golang-migrate).
Explore the method : Database MigrationsRetrieve Databento market data through an available MCP server: dataset/schema discovery, symbol resolution, historical bars, quotes, reference data, and bounded batch-download planning. Use for Databento data requests or supplying futures analysis with timestamped observations. Requires a separately configured server, API key, dataset entitlements, and any necessary data budget.
Explore the method : Databento market dataUse when building public-equity DCF valuation workbooks. Default to the banker formula workbook path for new model builds; use deterministic exports only for controlled support calculations or explicit lightweight runs. Do not use for standalone workbook audits; use model-audit-tieout.
Explore the method : Dcf Model BuilderUse when debugging issues.
Explore the method : Debug ZoomUse when isolating failures.
Explore the method : Debug Zoom IntegrationTurn a loose idea into a sequenced map of investigation tickets, then drive them to resolution one at a time.
Explore the method : Decision mapCapture a settled or proposed decision as a durable decision record after a discussion, design review, planning session, or pressure test. Use when the user wants to preserve context, alternatives, ownership, consequences, open questions, or glossary terms without silently treating an unresolved proposal as approved.
Explore the method : Decision recordUse when running first-pass QC on Public Equity Investing decks or reports. Do not use as external-circulation certification.
Explore the method : Deck Report QcDeFi yield evaluation including fee APR, real vs nominal yield, net APY after costs, and yield sustainability analysis. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Defi Yield AnalysisHelp the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.
Explore the method : Define goalDeploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
Explore the method : Deploy to VercelDeployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.
Explore the method : Deployment PatternsComprehensive design skill: brand identity, design tokens, UI styling, logo generation (55 styles, Gemini, Atlas Cloud, or MuAPI AI), corporate identity program (50 deliverables, CIP mockups), HTML presentations (Chart.js), banner design (22 styles, social/ads/web/print), icon design (15 styles, SVG, Gemini 3.1 Pro), social photos (HTML→screenshot, multi-platform). Actions: design logo, create CIP, generate mockups, build slides, design banner, generate icon, create social photos, social media images, brand identity, design system. Platforms: Facebook, Twitter, LinkedIn, YouTube, Instagram, Pinterest, TikTok, Threads, Google Ads.
Explore the method : DesignPremium UI/UX design audit and refinement skill. Conducts systematic visual audits of existing apps and produces phased, implementation-ready design plans. Use this skill whenever the user asks to audit a UI, improve an app's visual design, make an interface feel more polished or premium, review design consistency, fix visual hierarchy, or refine spacing/typography/color. Also trigger when the user says "design review", "make it look better", "UI polish", "visual refinement", "design pass", "audit the design", or references making an app feel more professional. This skill is purely visual — it does not touch functionality, logic, or features. It elevates what exists.
Explore the method : Design auditReverse-engineer the design taste of any website. Given a URL, captures DOM data and a screenshot via real browser, then runs a 4-step analysis to produce {domain}.md + {domain}.json with practical design tokens (colors, typography, spacing, radii, shadows, grid) AND taste DNA (Trigger → Decision → Reason → Evidence trade-offs explaining WHY the design works). Use whenever the user wants to extract a site's design system, study a competitor's visual language, port an aesthetic to a new project, or generate design guidance for an AI coding agent. Triggers on '/taste <url>', 'analyze the design of X', 'what makes X's site good', 'extract design tokens from X', 'give me X's design DNA', 'build something in the style of X', 'I want my app to feel like X'. Output rejects AI slop ('clean', 'modern', 'user-friendly') in favor of concrete px/hex values and restraint trade-offs. Do NOT use for scraping data, summarizing page content, or tasks unrelated to visual design.
Explore the method : Design DNA extractionRead a Canva design and return structured, actionable design feedback — visual hierarchy, copy/messaging, layout & spacing, consistency, readability, and accessibility. Read-only; makes no changes to the design. Use when the user asks to "review my design", "give me feedback on this", "critique my deck/poster/flyer", "how can I improve this design", or "what's wrong with this slide".
Explore the method : Design feedbackDesign KPI frameworks, metric definitions, targets, guardrails, and measurement plans for product or business decisions. Use when success metrics, drivers, guardrails, targets, or the measurement approach need to be defined or improved.
Explore the method : Design KpisToken architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.
Explore the method : Design systemReverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.
Explore the method : Design vocabularySolana DEX swap execution via Jupiter aggregator including quoting, transaction building, signing, and confirmation. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Dex Execution PlanningAMM pool mechanics comparison across Raydium, Orca, and Meteora including fee structures, pool types, creation patterns, and volume efficiency. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Dex Pool AnalysisDiagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
Explore the method : Diagnosing BugsUse when a superpowers session went wrong and your human partner wants to know why — repeated work, ignored plans, stumbles, poor results, a skill that didn't fire, "it took too long", "why is it so expensive", "what is it doing" — or wants to build a bug report for the superpowers maintainers, for the current session or a past one identified by id or path, on any harness.
Explore the method : Diagnosing SuperpowersComprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.
Explore the method : Diagram planningWhen the user wants to submit their product to startup, SaaS, AI, agent, MCP, no-code, or review directories for backlinks, domain rating, and discovery. Also use when the user mentions "directory submissions," "submit to directories," "backlinks from directories," "list my product," "submit to Product Hunt," "BetaList," "TAAFT," "Futurepedia," "G2 listing," "Capterra listing," "AlternativeTo," "SaaSHub," "AI directories," "MCP registry," "publish my MCP server," "awesome list," "llms.txt directory," "Claude plugin directory," "agent directory," "dofollow backlinks," "launch directories," or "directory tracker." Use this whenever someone is planning the directory layer of a product launch or an ongoing backlink campaign. For the broader launch moment, see launch. For programmatic SEO pages that should live behind these backlinks, see programmatic-seo. For AI citation optimization, see ai-seo.
Explore the method : Directory SubmissionsAfter you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.
Explore the method : Discernment NudgeUse when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Explore the method : Dispatching Parallel AgentsUse this skill to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Dividend Growth Pullback ScreenerGuide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Explore the method : Doc CoauthoringCreate, edit, redline, and comment on `.docx`, Word, and Google Docs-targeted document artifacts inside the container, with a strict render-and-verify workflow. Use `render_docx.py` to generate page PNGs (and optional PDF) for visual QA, then iterate until layout is flawless before delivering the final document.
Explore the method : DocumentsUse this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Explore the method : DOCXAnalyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Downtrend Duration AnalyzerDesign advisory system channeling Hirohiko Araki's JoJo's Bizarre Adventure visual language. Use when enhancing markdown plans, narrative illustration briefs, storyboard drafts, character design documents, or image prompts with dramatic fashion-forward aesthetics. Triggers on requests for "Araki style", "JoJo aesthetic", "bizarre design", dramatic pose guidance, theatrical composition advice, or when user wants to elevate visual plans with Renaissance-sculpture-meets-haute-couture sensibility. Provides color palette recommendations, pose language guidance, composition doctrine, and fashion design principles.
Explore the method : Dramatic fashion and illustration designEvaluate account-level drawdown circuit breaker rules from trader-memory-core state and decide whether new trade risk is allowed today. Uses realized P&L, losing-streak cooldowns, and weekly/monthly drawdown limits without any external API. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Drawdown Circuit BreakerUse when analyzing public-company earnings after results, guidance, transcript, or call commentary. Do not use for pre-print previews.
Explore the method : Earnings analysisRetrieve upcoming company earnings dates, announcement timing, and EPS estimates for a watchlist. Select FMP or the staskh market-data adapter according to available tools; preserve unknown timing and distinguish calendar research from earnings-results analysis.
Explore the method : Earnings CalendarUse when analyzing public-company earnings after results, guidance, transcript, or call commentary. Do not use for pre-print previews.
Explore the method : Earnings Deep DiveUse when preparing full pre-earnings preview reports with executive summary, expectation bar, guidance credibility, KPI dashboard, scenarios, and call questions. Do not use after results or for short summaries unless the user explicitly asks for a summary/short version.
Explore the method : Earnings PreviewAnalyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Earnings Trade AnalyzerRetrieve an economic calendar or assess scheduled events against futures positions with historical reaction context. Select the FMP, FXMacroData, or NinjaTrader event-watch method; preserve timezone, event status, and uncertainty in reactions.
Explore the method : Economic Calendar FetcherUse when translating a specific event, policy change, macro shock, or industry development into public-equity issuer, sector, earnings, valuation, positioning, and portfolio implications. Do not use for standalone macro strategy, rates, FX, credit, futures, or generic market commentary.
Explore the method : Economic impact analysisUse when translating a specific event, policy change, macro shock, or industry development into public-equity issuer, sector, earnings, valuation, positioning, and portfolio implications. Do not use for standalone macro strategy, rates, FX, credit, futures, or generic market commentary.
Explore the method : Economic Impact ReportGenerate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Candidate AgentAbstract detector tickets and hints into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design/export. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Concept SynthesizerExtract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Hint ExtractorOrchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Pipeline OrchestratorAggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Signal AggregatorConvert abstract edge concepts into strategy draft variants and optional exportable ticket YAMLs for edge-candidate-agent export/validation. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Strategy DesignerCritically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism. Use when strategy_drafts/*.yaml exists and needs quality gate before pipeline export. Outputs PASS/REVISE/REJECT verdicts with confidence scores. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Edge Strategy ReviewerEdit and improve articles by restructuring sections, improving clarity, and tightening prose. Use when user wants to edit, revise, or improve an article draft.
Explore the method : Edit writingDiagnostic and advisory skill for email deliverability problems. Use when a developer asks why emails are going to spam, not reaching the inbox, getting blocked, bouncing, or how to improve sender reputation — with or without a specified platform. Covers SendGrid-specific tooling: SPF, DKIM, DMARC, BIMI, IP warmup, list hygiene, bounce/spam rate thresholds, and Engagement Quality Score (SEQ). Do NOT use for Twilio Email (comms.twilio.com / Account SID + Auth Token) — use twilio-email-deliverability-advisor instead. Do NOT use for general email sending questions — use twilio-sendgrid-email-send (SendGrid) or twilio-email-deliverability-advisor instead.
Explore the method : Email deliverability triageEvidence-first mailbox triage, drafting, send verification, and sent-mail-safe follow-up workflow for ECC. Use when the user wants to organize email, draft or send through the real mail surface, or prove what landed in Sent.
Explore the method : Email draftingWhen the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. Emails avoid AI tells like 'it's not X, it's Y' reveals, 'no X, no Y, no Z' lists, and fake 'Re:' subject lines. For cold outreach emails, see cold-email. For in-app onboarding, see onboarding.
Explore the method : EmailsA set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
Explore the method : Employee faqPlaywright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies.
Explore the method : End-to-end testingCodified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.
Explore the method : Energy procurement planningSafely update public-company Excel model copies from source-to-model maps; emits XLSX as the hero artifact and CSV/log/manifest as support. Do not use for pure earnings notes or broad workbook audits.
Explore the method : Equity Model UpdateUse when analyzing dated public-equity event paths, probabilities, payoffs, and expected returns. Do not use for generic catalyst lists, risk sizing, hedges, capital structure, covenants, or credit recovery.
Explore the method : Event Driven AnalyzerWhen the user wants to plan, run, sponsor, speak at, or get pipeline from events — webinars, conferences, trade shows, meetups, dinners, workshops, virtual summits, or user conferences. Also use when the user mentions 'event marketing,' 'field marketing,' 'run a webinar,' 'webinar funnel,' 'show-up rate,' 'should we sponsor,' 'sponsor a conference,' 'trade show booth,' 'booth strategy,' 'event ROI,' 'badge scans,' 'event follow-up,' 'speaking slot,' 'CFP,' 'conference talk,' 'host a dinner,' 'user conference,' or 'virtual summit.' Covers all four roles: hosting, sponsoring/exhibiting, speaking, and attending. For product launch moments, see launch. For the partnership side of joint webinars, see co-marketing. For ongoing community programs, see community-marketing. For podcast appearances, see public-relations. For the email sequences themselves, see emails.
Explore the method : EventsEvaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
Explore the method : Evidence critiqueManage evidence freshness by identifying stale decisions and providing governance actions
Explore the method : Evidence freshness reviewControl an open or active Microsoft Excel workbook through the ChatGPT add-in or connected session. Use when the user tags the Microsoft Excel app in Codex or follows up on an established live Excel task. Do not use for standalone spreadsheet files or Google Sheets.
Explore the method : Excel Live ControlUse when you have a written implementation plan to execute in a separate session with review checkpoints
Explore the method : Executing PlansReinforcement learning for trade execution optimization including order splitting, adaptive timing, and impact minimization. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Execution Optimization ResearchProduce a concise decision-ready executive summary, one-pager, leadership brief, or plain-English distillation from research, findings, a transcript, a proposal, or other source material. Use when the reader is time-constrained and needs the recommendation, evidence, uncertainty, and next step without working through the full source.
Explore the method : Executive summaryTurn executive travel, event, and meeting context into an owned readiness plan with an evidence ledger, backward-planned milestones, briefing requirements, decision gates, communications, risks, and post-trip follow-through. Use for complex multi-city travel or high-stakes executive trips.
Explore the method : Executive trip chief-of-staff planDesign, critique, prepare for, or learn from an executive workshop that must move a cross-functional group from understanding to a practical decision or commitment. Use for facilitated executive experiences, not event logistics, ordinary meetings, generic brainstorming, or long-running courses.
Explore the method : Executive workshop designEvaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
Explore the method : Experiment designGenerate a one-page Market Posture summary with net exposure ceiling, growth-vs-value bias, participation breadth, and new-entry-allowed vs cash-priority recommendation by integrating signals from breadth, regime, and flow analysis skills. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Exposure CoachCreates and inspects Fabric Activator (Reflex) alerts: rules, their data sources, conditions, and the Teams or email action they fire, plus decoding an existing ReflexEntities definition. Choose it whenever someone wants to be told, or something done, when values cross a threshold. The pipeline delivering those values is eventstream-cli.
Explore the method : Fabric activatorFabric api troubleshooting. Diagnose and resolve Microsoft Fabric REST API performance issues including HTTP 429 throttling, long running operation (LRO) timeouts, pagination bottlenecks, and slow API response times. Use when remediate Fabric API latency, capacity throttling, retry-after handling, operation polling failures, bulk API call optimization, or automating Fabric REST API diagnostics with PowerShell. Covers api.fabric.microsoft.com endpoint performance, Entra ID token acquisition delays, and Fabric capacity SKU rate limits. Diagnose and resolve Microsoft Fabric REST API errors including HTTP 401 Unauthorized, 403 Forbidden, 404 Not Found, 429 Throttling, and 5xx server errors. Use when debugging Fabric API authentication failures, Entra ID token issues, insufficient scopes, throttling/rate limiting, long running operation (LRO).
Explore the method : Fabric api troubleshootingBrings Azure Monitor, Application Insights, and Log Analytics telemetry into Fabric as Eventhouse external delta tables and correlates it with business data. Use to onboard observability data, judge whether latency or availability affected revenue, or build a Real-Time dashboard and Operations Agent over it.
Explore the method : Fabric azmon mirroredcatalogs operationsAnswers natural-language business questions over existing Power BI reports and semantic models through the FabricIQ MCP endpoint, discovering artifacts, resolving entities, generating the DAX itself, and returning a plain-language answer. Use when the user asks a data question, including one that says to query the model directly. For DAX the user supplies, or measures to save, use semantic-model-authoring.
Explore the method : Fabric business data analysisFabric data agent workflow. Create, configure, and manage Microsoft Fabric Data Agents that enable natural language Q&A over lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies. Use when asked to build data agents, configure NL2SQL/NL2DAX/NL2KQL experiences, write agent instructions, create example queries, automate data agent provisioning via REST API or PowerShell, integrate Fabric data agents with Azure AI Foundry, or troubleshoot data agent configuration issues. Diagnose and resolve Microsoft Fabric Data Agent performance issues including slow query generation, capacity throttling (HTTP 430), Spark session startup delays, KQL/SQL/DAX query timeouts, data source misconfiguration, example query validation failures, resource profile tuning, VOrder optimization, autotune settings, and Lakehouse table.
Explore the method : Fabric data agent workflowDiagnose and resolve Microsoft Fabric Data Factory pipeline performance issues. Use when pipelines are slow, copy activities timeout, dataflows stall, activities are stuck, throughput is low, capacity is throttled, or jobs queue indefinitely. Covers copy activity tuning (parallelCopies, DIU, ITO, partitioning), pipeline monitoring via Monitoring Hub and workspace monitoring, Spark job queueing, capacity SKU limits, error code resolution, and dataflow optimization. Keywords include Fabric pipeline slow, copy activity performance, Data Factory throttling, pipeline timeout, activity stuck, TooManyRequestsForCapacity, HTTP 430, pipeline troubleshoot, dataflow performance, copy parallelism, intelligent throughput optimization.
Explore the method : Fabric data factory perf remediatePorts existing Databricks notebooks and jobs to Fabric, covering dbutils to notebookutils, secret scopes to Key Vault, DBFS to OneLake, Unity Catalog mapping to schema-enabled Lakehouses, Jobs and Delta Live Tables to Spark Job Definitions and Pipelines, and Photon to the Native Execution Engine. Use whenever Databricks code has to be converted. For Fabric notebook code that is not being migrated, use spark-cli.
Explore the method : Fabric databricks migrationFabric dataflows workflow. Diagnose and resolve Microsoft Fabric Dataflow Gen2 performance issues including slow refresh times, Fast Copy optimization, query folding failures, staging bottlenecks, gateway latency, incremental refresh tuning, capacity throttling, and data destination write performance. Use when troubleshooting dataflow refresh failures, optimizing Dataflow Gen2 execution time, debugging Power Query mashup engine performance, resolving staging Lakehouse or Warehouse compute issues, configuring Fast Copy connectors, fixing insufficient permissions errors, monitoring dataflow refresh history, or automating dataflow health checks via REST API and PowerShell. Manages Fabric Dataflow Gen2 items, including creation, M editing, connections, output destinations, getDefinition and updateDefinition inspection, executeQuery, refresh.
Explore the method : Fabric dataflows workflowManages Fabric deployment pipelines for ALM promotion across dev, test, and prod stages, including stage creation, workspace assignment, selective forward or backward deploys, operation polling, stage role assignments, and the pipeline and workspace permissions each action requires. For Git sync use git-integration-operations-cli.
Explore the method : Fabric deployment pipelines authoringEstimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
Explore the method : Fabric e2e fabric cost estimationPlans and builds end-to-end Fabric data platforms on the Bronze, Silver, and Gold medallion pattern: ingestion to analytics, PySpark and Delta layers, pipeline orchestration, per-layer Spark tuning, Materialized Lake View versus notebook choices for Silver and Gold, and Direct Lake handoff. Use for layered architecture design, planning and recommendations. For questions over an existing Power BI report use fabriciq.
Explore the method : Fabric e2e medallion architectureFabric eventhouse workflow. Diagnose and resolve performance issues in Microsoft Fabric Real-Time Intelligence including Eventhouse, KQL databases, Eventstream, and ingestion pipelines. Use when asked to troubleshoot slow KQL queries, high Eventhouse CPU or memory, ingestion latency or failures, Eventstream throughput problems, capacity throttling (HTTP 430), cache policy tuning, materialized view lag, or Always-On configuration. Covers workspace monitoring, Fabric Capacity Metrics app, query optimization, and streaming diagnostics. Authors and queries Fabric Eventhouse and KQL databases: tables, functions, policies, materialized views, ingestion, and read-only KQL for real-time and time-series analytics. All KQL database work belongs here, table creation and simple queries included. An Eventstream item is eventstream-cli; an Event.
Explore the method : Fabric eventhouse workflowGoverns Event Schema Sets, the Fabric registries of event types and message payload schemas. Covers create, rename, update and delete of a definition, and listing, property reads or base64 decoding. Every Event Schema Set task belongs here. Ingestion pipelines are eventstream-cli.
Explore the method : Fabric eventschemasetOwns Fabric Eventstream items end to end: sources, operators, destinations, routing, retention, throughput and connection strings, plus read-only topology checks. Pick it for anything that names an Eventstream, creating and listing included. Where the rows finally land is eventhouse-cli.
Explore the method : Fabric eventstreamRuns the Fabric Git integration lifecycle through fab api or az rest, including connecting a workspace to Azure DevOps or GitHub, committing, updating from Git, reading sync status, resolving conflicts, disconnecting a connected workspace, and automating sync with a service principal. For stage promotion use deployment-pipelines-authoring-cli. Branch switching, fab deploy, fabric-cicd and cross-workspace rebinding are out of scope.
Explore the method : Fabric git integration operationsPorts HDInsight Spark and Hive workloads to Fabric, converting HiveContext and SparkContext to SparkSession, WASB and ABFS paths to OneLake shortcuts, Hive DDL to Delta tables in a Lakehouse, and Oozie coordinators and actions to pipeline activities and triggers. Use when the source workload is HDInsight.
Explore the method : Fabric hdinsight migrationTroubleshoot Microsoft Fabric Lakehouse access control issues including OneLake security roles, SQL analytics endpoint permissions, workspace roles, data access roles, row-level security (RLS), column-level security (CLS), object-level security (OLS), dynamic data masking, shortcut permissions, Direct Lake security integration, DefaultReader role, ReadAll permission, and OneLake data access role conflicts. Use when users report permission denied, unauthorized access, missing data, empty query results, or cannot see tables in Fabric Lakehouse.
Explore the method : Fabric lakehouse access controlFabric lakehouse performance. Diagnose and resolve Microsoft Fabric Lakehouse performance issues including slow Spark queries, small file problems, Delta table fragmentation, V-Order configuration, table maintenance (OPTIMIZE, VACUUM, Z-Order), SQL analytics endpoint tuning, Direct Lake performance, resource profile selection, autotune configuration, capacity throttling, and streaming ingestion optimization. Use when asked to troubleshoot Fabric Lakehouse slowness, optimize Delta tables, fix small file problems, configure Spark settings, run table maintenance, or improve query performance in notebooks or pipelines. Diagnose and resolve Microsoft Fabric OneLake performance issues including slow queries, cold cache latency, small file problems, Delta table fragmentation, V-Order optimization, Spark throttling, capacity SKU sizing, and.
Explore the method : Fabric lakehouse performanceDiagnose and resolve Microsoft Fabric network performance issues including connectivity failures, latency, private endpoints, managed VNets, outbound access protection, gateway diagnostics, OneLake endpoint routing, service tag configuration, firewall allowlisting, DNS resolution, and Spark session startup delays. Use when remediate Fabric networking, slow Spark jobs, connection timeouts, private link errors, managed private endpoint approval, or capacity throttling.
Explore the method : Fabric network remediateGoverns Microsoft Fabric OneLake catalog health, protection, and trust through Fabric Admin, Core, and Power BI REST APIs. Use for tenant or owner-scoped audits and guarded remediation of domains, workspace assignment, capacity, labels, tags, descriptions, refresh, and item identity. Catalog item discovery belongs to search-consumption-cli.
Explore the method : Fabric onelake catalog governFabric ontology workflow. Guide for working with Microsoft Fabric IQ (preview), the semantic intelligence workload for unified data and business vocabulary. Use when creating or managing ontology items, defining entity types, binding data to ontologies, creating relationship types, configuring data agents with ontology sources, working with Graph in Microsoft Fabric, managing Fabric IQ tenant settings, querying ontology graphs, generating ontologies from Power BI semantic models, or remediate Fabric IQ preview features. Covers ontology, graph, data agent, operations agent, and semantic model items. Manages Fabric IQ Ontology items, including entity and relationship types, data bindings, and definition updates, plus schema, lineage, grounding, and graph-walk exploration. Use for ontology modelling and traversal. For natural-language.
Explore the method : Fabric ontology workflowTroubleshoot and optimize pandas performance in Microsoft Fabric Spark notebooks. Use when diagnosing slow pandas operations, toPandas() out-of-memory errors, pandas API on Spark (pyspark.pandas) bottlenecks, DataFrame conversion failures, collect() memory issues, driver memory exhaustion, notebook cell timeouts, or when optimizing pandas workloads for Fabric capacity. Covers pandas vs Spark DataFrame conversion, memory profiling, broadcast joins, shuffle tuning, resource profiles, and Native Execution Engine integration.
Explore the method : Fabric pandas perf remediateMonitor and optimize Microsoft Fabric capacity, Spark compute, and workload performance. Use when asked to check capacity utilization, diagnose throttling (HTTP 430), monitor Spark VCore consumption, analyze CU usage, review Monitoring Hub jobs, query Fabric REST APIs for capacity health, generate performance reports, tune Spark resource profiles, investigate concurrency limits, or optimize Fabric SKU sizing. Supports PowerShell, T-SQL, and REST API workflows.
Explore the method : Fabric performance monitoringMigrates Synapse Data Factory pipelines to Fabric Data Factory, turning linked services into Fabric connections, inlining datasets into activities, converting global parameters to Variable Libraries, and mapping SynapseNotebook to TridentNotebook. Use for pipeline JSON. For Synapse Spark notebook code use synapse-migration.
Explore the method : Fabric pipeline migrationOrchestrate Project Osmos for long-running Fabric and OneLake data-engineering outcomes, including task status, messages, follow-ups, cancellation, and deletion. Osmos tasks are remote service tasks, not local background agents. In Fabric portal Copilot, direct interaction is unsupported; point users to GitHub Copilot CLI. Use spark-cli for direct non-Osmos Spark work. Triggers: "Project Osmos", "Project Osmos task status", "check my Osmos task".
Explore the method : Fabric project osmosFinds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.
Explore the method : Fabric search consumptionFabric spark performance. Troubleshoot and optimize Delta Lake and Apache Spark performance in Microsoft Fabric. Use when diagnosing slow Spark jobs, small file problems, data skew, shuffle bottlenecks, out-of-memory errors, V-Order tuning, OPTIMIZE/VACUUM operations, partition strategy, resource profile selection (writeHeavy, readHeavyForSpark, readHeavyForPBI), autotune configuration, Native Execution Engine, broadcast joins, AQE (Adaptive Query Execution), or when Spark notebooks or Spark Job Definitions run slower than expected in Fabric Lakehouse workloads. Diagnose and resolve performance issues in Microsoft Fabric notebooks running Apache Spark. Use when notebooks are slow, Spark jobs are timing out, sessions hit HTTP 430 throttling errors, data skew causes straggling tasks, shuffle operations are inefficient, Delta Lake tables.
Explore the method : Fabric spark performanceFabric spark workflow. Troubleshoot Microsoft Fabric materialized lake views (MLV) performance issues including slow refresh, incremental refresh failures, full refresh fallback, Spark job failures, lineage execution errors, data quality constraint violations, and optimal refresh configuration. Use when diagnosing MLV refresh duration, monitoring MLV runs in Monitor Hub, analyzing Spark logs for MLV failures, enabling change data feed (CDF), resolving delta table not found errors, or optimizing MLV query definitions for incremental refresh eligibility. Covers Spark SQL syntax, TBLPROPERTIES, PARTITIONED BY, CONSTRAINT CHECK ON MISMATCH DROP/FAIL, and custom environment configuration. Manages Fabric Spark work, including notebook cell code with %%configure, %%sql, PySpark and notebookutils, named notebook runs, Livy sessions, triage of.
Explore the method : Fabric spark workflowManages a Fabric SQL database item, the OLTP SQL Server engine, including running T-SQL through sqlcmd, temporal and vector similarity search, schema and sys.tables inspection, dacpac deployment, and Query Store, blocked sessions and regressed-plan investigation. Use for any query or change against a SQL database item. Warehouse, lakehouse SQL endpoint and mirrored items belong to sqldw-cli.
Explore the method : Fabric sqldbManage Fabric Warehouse, Lakehouse SQL endpoints, and Mirrored Databases: DDL/DML, COPY INTO, read-only T-SQL, Query Insights diagnostics, and Capacity Metrics CU-spike correlation. Synapse migration target SQL belongs to synapse-migration; Fabric SQL database belongs to sqldb-cli. Triggers: query warehouse, create warehouse table, failed or canceled query, CU spike, Capacity Metrics app, custom SQL pool, Lakehouse table health.
Explore the method : Fabric sqldwConverts Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.
Explore the method : Fabric synapse migrationDiagnose and resolve performance issues with Microsoft Fabric User Data Functions. Use when functions are slow, timing out, returning errors, consuming excessive capacity units, or exhibiting cold start latency. Covers execution timeouts, response size limits, connection bottlenecks, logging analysis, capacity metrics monitoring, invocation diagnostics, Python optimization, and UDF service limit remediate.
Explore the method : Fabric udf perf remediateManages Fabric Variable Library items, including definitions, libraryVariables and valueSets overrides. Covers which consumers can reference a variable and with what syntax -- pipelines, notebooks via notebookutils variableLibrary, Dataflow Gen2, Copy Jobs, shortcuts, UDFs and Plan items -- the active value set per workspace or deployment stage, and Git serialization for CI/CD.
Explore the method : Fabric variable libraryUse this skill to validate the "why" before building, run product diagnostics, and pressure-test product direction before the request becomes an implementation contract.
Explore the method : Feature prioritizationUse when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Explore the method : Feedback responseIntegrate with Figma API for design automation and code generation. Use when extracting design tokens, generating React/CSS code from Figma components, syncing design systems, building Figma plugins, or automating design-to-code workflows. Triggers on Figma API, design tokens, Figma plugin, design-to-code, Figma export, Figma component, Dev Mode.
Explore the method : Figma design automationUse when auditing existing Public Equity Investing models or spreadsheets. Do not use to build a new model from scratch.
Explore the method : Financial assumption reviewUse when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup.
Explore the method : Financial statement comparabilityUse when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup.
Explore the method : Financials NormalizerUse when implementation is complete, all tests pass, and you need to decide how to integrate the work
Explore the method : Finishing A Development BranchBuild and open FinViz screener URLs from natural language requests. Use when user wants to screen stocks, find stocks matching criteria, filter by fundamentals or technicals, or asks to open FinViz with specific conditions. Supports both Japanese and English input (e.g., "高配当で成長している小型株を探したい", "Find oversold large caps with high ROE"). Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Finviz ScreenerSystematic Fishbone analysis exploring problem causes across six categories
Explore the method : Fishbone analysisIterative Five Whys root cause analysis drilling from symptoms to fundamentals
Explore the method : Five whysWhen the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," "free resource," "ROI calculator," "grader tool," "audit tool," "should I build a free tool," or "tools for lead gen." Use this whenever someone wants to build something useful and give it away to attract leads or earn links. For downloadable content lead magnets (ebooks, checklists, templates), see lead-magnets.
Explore the method : Free ToolsCodified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.
Explore the method : Freight negotiation planningGuidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.
Explore the method : Frontend DesignFrontend development patterns for React, Next.js, state management, performance optimization, and UI best practices.
Explore the method : Frontend PatternsDetects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology. Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring. Use when user asks about market bottom signals, follow-through days, rally attempts, re-entry timing after corrections, or whether it's safe to increase equity exposure. Complementary to market-top-detector (defensive) - this skill is offensive (bottom confirmation). Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : FTD DetectorTranslates natural-language alert intent into a validated Tradovate alert-DSL expression. Submits the expression via create_alert.expression. Covers 37 numeric functions across Account (netLiq, dollarOpenPL, dailyLossLimit, ...) and Contract (lastPrice, percentChange, settlementPrice, ...) entities. Also covers Position (netPos, posOpenPLUsd, posInitMarginUsd, ...) and Currency (currentRate) entities. Supports arithmetic (plus, minus, times, divide, parens), comparison (gt, gte, lt, lte, eq, neq), and top-level AND/OR/XOR logic. Validates the expression offline (subject syntax, function catalog, paren balance, flat-logic rule) before submission. Use when the user says "alert", "notify", "notify me", "watch for", "wake me when", "tell me when", "heads up when", "ping me if", "set a trigger", or "let me know if. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Futures Alerts ComposerSymbol discovery, front-month resolution, rollover-imminence classification, and product-family awareness (full ↔ micro pairs like ES/MES) for futures contracts. Resolves bare product codes like "ES" or "NQ" to the actual tradeable front-month contract (e.g., ESU6) by open-interest rank across maturities. Classifies rollover status as not_imminent / approaching / imminent / rolled, with calendar-spread candidate flag when both legs are liquid. Use when the user asks "what symbol", "which contract to trade", "front month", "roll" or "rollover", "micro" or "full", or "calendar spread". Also use when the user gives a bare product code (ES, NQ, YM, CL, GC, etc.) without a maturity. Also handles product-family metadata via search_contracts includeFamilySiblings flag. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Futures Contract IntelligenceComputes market metrics for futures from market_history output. Metrics include session VWAP, POC/VAH/VAL (value area), bar-level delta, cumulative delta, order-flow imbalance, ATR(N), realized volatility, and cross-symbol relative strength. It produces narrative summaries of market structure and the volatility regime. Use when the user asks about VWAP, POC, market profile, or value area. Also use for ATR, realized volatility, delta, cumulative delta, or order-flow imbalance. Also use for the volatility regime, or for intra-day relative strength between related symbols (e.g., ES vs NQ vs RTY). position-watchdog, pretrade-risk, scale-manager, chart-render, trade-replay, and trade-debrief consume this skill's output as input. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Futures Market ContextCalculate futures contract quantity from account risk, stop distance, tick/point value, and contract constraints. Includes NinjaTrader pretrade-risk with filled plus pending exposure and a required estimate_order feasibility gate before a bracket proposal. Offline arithmetic is labeled unverified by the server.
Explore the method : Futures Position SizerGather business context from connected or provided sources so downstream analysis starts with the right framing. Use when an analytical question depends on missing context, such as what a metric means, what changed recently, or which sources should be checked. If the same prompt asks for diagnosis, recommendation, or a deliverable, gather context first and continue to the focused skill.
Explore the method : Gather Business ContextPlan and operate a GenStore storefront using a supplied brand brief, product facts, catalog, and authorized account. Covers store design, listings, channel readiness, and launch verification; returns a proposal when no working GenStore connection is available.
Explore the method : GenStore storefront workflowGit workflow patterns including branching strategies, commit conventions, merge vs rebase, conflict resolution, and collaborative development best practices for teams of all sizes.
Explore the method : Git workflowFetch and display GitHub trending repositories and developers. Use when building dashboards showing trending repos, discovering popular projects, or tracking GitHub trends. Triggers on GitHub trending, trending repos, popular repositories, GitHub discover.
Explore the method : Github trending researchTurn one to three committed market or campaign plans into an executable go-to-market kickoff manual. Use when a team asks for a 30-, 60-, or 90-day operating plan, launch manual, sales-motion kickoff, or a practical answer to what the team should do first.
Explore the method : Go to market kickoffPrompt- and template-complete Google Docs creation and editing with explicit-instruction-authoritative structural preservation, including semantic roles, relationships, comparison dimensions, and instructed extensions; full-topology native-copy routing; source-grounded per-tab adaptation for past/example references; style-preserving hyperlink and table edits; canonical smart-chip-first authoring for dates and relevant supported people or Google resources; a file-backed advisory trusted read before existing-document writes; automatic protected-control awareness; direct connector APIs by default; DOCX-first import only when no supplied Google Doc template/reference constrains the output; and checked-in code mode only for exact native dropdown mutation. Use when Codex must create, edit, fill, adapt, redesign, or verify Google Docs without overriding explicit user/template instructions, adding unrequested document scope, or carrying stale reference facts into a new deliverable.
Explore the method : Google DocsUse connected Google Drive as the single entrypoint for Drive, Docs, Sheets, and Slides work. Use when the user wants to find, fetch, organize, share, export, copy, or delete Drive files, or summarize and edit Google Docs, Google Sheets, and Google Slides through one unified Google Drive plugin.
Explore the method : Google DriveWrite, reply to, and resolve Google Drive comments on Docs, Sheets, Slides, and Drive files with evidence-backed location context. Use when the user asks to leave comments, review a file with comments, respond to comment threads, or resolve Drive comments.
Explore the method : Google Drive CommentsAnalyze and edit connected Google Sheets with range precision. Use when the user wants to create Google Sheets, find a spreadsheet, inspect tabs or ranges, search rows, plan formulas, create or repair charts, clean or restructure tables, write concise summaries, or make explicit cell-range updates.
Explore the method : Google SheetsRoute Google Slides authoring requests and derive a design system from a native template or reference deck. Use this skill when the user provides an existing native Google Slides deck as a template, reference, or prior-period source, or asks to edit, update, repair, restyle, or clean up an existing native Google Slides deck. Use the Presentations skill instead for net-new presentation creation when no existing native Google Slides deck must be followed.
Explore the method : Google SlidesInteract with all Google Workspace APIs via the gws CLI. Use when managing Drive files, sending/reading Gmail, creating Calendar events, reading/writing Sheets/Docs/Slides, managing Chat spaces, contacts, Admin users/groups, Vault eDiscovery, Classroom, Apps Script, Workspace Events, or configuring the gws MCP server. Triggers on Google Workspace, gws, Drive, Gmail, Calendar, Sheets, Docs, Slides, Chat, Tasks, Meet, Forms, Keep, Admin, People, Vault, Classroom, Apps Script, Cloud Identity, Alert Center, Groups Settings, Licensing, Reseller, Model Armor, gws CLI, gws mcp, Google API, Workspace automation, npx skills add.
Explore the method : Google workspace operationsDevelop evidence-based research or program grant proposals from a supplied funding opportunity. Use for proposal outlines, technical narratives, specific aims or objectives, impact statements, workplans, budgets, resubmissions, and compliance-gap checks. Does not submit an application, assert eligibility, or invent commitments, results, or agency requirements.
Explore the method : Grant proposalCalculate or interpret option delta, gamma, theta, vega, implied volatility, and basic European Black-Scholes values from explicit contract inputs. The additional upstream options-pricing source is partial; planned American/exotic pricing, volatility surfaces, and exchange integrations are not available features.
Explore the method : GreeksUse when a user asks to be grilled, challenged, pressure-tested, or to find holes in a plan, design, proposal, workflow, automation, skill, operating decision, or client approach before implementation. Runs a rigorous one-question-at-a-time interview.
Explore the method : GrillingUse before claiming a code change is complete. Scans changed files for hardcoded values that should be dynamic — external API constants, contract addresses, URLs, tunables, schedule times, magic numbers with domain meaning. Language-agnostic. Outputs offenders with suggested fixes.
Explore the method : Hardcode AuditStructured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Explore the method : Hypothesis developmentFind and place the best 0DTE (zero-days-to-expiration) credit spreads from Interactive Brokers. Default execution route is the EMA9/EMA21 + VIX/VXN regime strategy (ema_vix_0dte.py), which auto-selects bull_put or bear_call from a bare EMA cross and skips the trade when the vol index is elevated (VXN ≥ 35 for NDX/QQQ, VIX ≥ 20 otherwise). Optional --rr-gate and --time-gate add red→red and bar-timing confirmation. Supports cash-settled indices (SPX, NDX, RUT, VIX) and any optionable stock/ETF. Manual spread type override is available via zero_dte.py. Requires TWS or IB Gateway running locally. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib 0DteGenerate tactical collar strategy reports for protecting PMCC positions through earnings or high-risk events. Requires TWS or IB Gateway running locally. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib CollarConsolidate IBRK trade CSV files from a directory into a summary report. Groups trades by symbol, underlying, date, strike, buy/sell, and open/close. Outputs both markdown and CSV. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib Create Consolidated ReportFind roll options for existing short positions OR find best covered call/put to open against long stock. Use when user asks about rolling shorts, finding roll candidates, writing covered calls, or managing option positions. Requires TWS or IB Gateway running locally. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib Find Short RollAnalyze PMCC (Poor Man's Covered Call / diagonal spread) positions from IB portfolio. For each diagonal spread, reports short leg risk (delta, IV, assignment probability), daily P&L projections, top-3 roll candidates, and a side-by-side comparison table. Requires TWS or IB Gateway running locally. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib Pmcc AdvisorReport delta-adjusted notional exposure across all IBKR accounts. Calculates option deltas using Black-Scholes and reports long/short exposure by account and underlying. Use when user asks about delta exposure, portfolio risk, or directional exposure. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib Report Delta Adjusted Notional ExposureFetch trade executions from Interactive Brokers filtered by account, date range, or symbol. Supports live API (~7 days history) and FlexReport (full history). Use when user asks about their trades, executions, or transaction history. Requires TWS or IB Gateway running locally. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Ib Trades HistoryDetect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : IBD Distribution Day MonitorUse when triaging public-equity idea candidates. Do not use for final trade recommendations, pitches, memos, or models.
Explore the method : Idea GenerationResearch and recommend an ideal customer profile, target segment, buying group, jobs to be done, exclusions, and validation plan. Use for ICP, target account criteria, who to target first, buyer profile, anti-ICP, segment prioritization, or go-to-market focus. Produces a cited decision artifact; it does not generate a prospect list or launch outreach.
Explore the method : Ideal customer profilePlanning skill for identity verification and fraud prevention. Qualifies the developer's needs across authentication method, channel selection, fraud risk level, and user experience to recommend the right Twilio Verify + Lookup architecture. Handles login, signup, password reset, and risk-adaptive verification.
Explore the method : Identity risk planningWhen the user wants to create, generate, edit, or optimize images for marketing — blog heroes, social graphics, product mockups, profile banners, listing visuals, or brand assets. Also use when the user mentions 'AI image generation,' 'generate an image,' 'create a graphic,' 'product mockup,' 'hero image,' 'social media graphic,' 'banner image,' 'cover photo,' 'profile banner,' 'listing screenshot,' 'Flux,' 'Flux Kontext,' 'Midjourney,' 'DALL-E,' 'GPT Image,' 'ChatGPT Images,' 'Ideogram,' 'Gemini image,' 'Nano Banana,' 'Recraft,' 'Stable Diffusion,' 'Canva,' 'Figma,' 'image optimization,' 'compress images,' 'WebP,' or 'OG image.' Use this for general-purpose marketing image creation and optimization. For paid ad image creative and platform-specific ad specs, see ad-creative. For video production, see video.
Explore the method : ImageCreate distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics. Based on Anthropic's Frontend Designer Skill
Explore the method : Impact designUse when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
Explore the method : ImpeccableImplement a piece of work based on a PRD or set of issues.
Explore the method : ImplementCreate structured plans for any multi-step task -- software features, research workflows, events, study plans, or any goal that benefits from structured breakdown. Also deepen existing plans with interactive review of sub-agent findings. Use for plan creation when the user says 'plan this', 'create a plan', 'write a tech plan', 'plan the implementation', 'how should we build', 'what's the approach for', 'break this down', 'plan a trip', 'create a study plan', or when a brainstorm/requirements document is ready for planning. Use for plan deepening when the user says 'deepen the plan', 'deepen my plan', 'deepening pass', or uses 'deepen' in reference to a plan. For exploratory or ambiguous requests where the user is unsure what to do, prefer ce:brainstorm first.
Explore the method : Implementation planRoute Data Analytics plugin-level requests and broad analytics work to the right focused workflow. Use when Data Analytics is at-mentioned, or for analytics requests involving data, metrics, dashboards, reports, charts, notebooks, spreadsheets, KPIs, market sizing, or semantic layers.
Explore the method : IndexWhen the user wants to run influencer, creator, or ambassador partnerships to promote their product — finding and vetting partners, structuring deals, briefing creators, disclosure compliance, and measuring ROI. Also use when the user mentions 'influencer marketing,' 'creator partnerships,' 'sponsorships,' 'YouTube sponsorships,' 'podcast sponsorships,' 'brand ambassador,' 'ambassador program,' 'creator program,' 'UGC creators,' 'tech UGC,' 'UGC creator program,' 'creator network,' 'B2B influencers,' 'thought leader ads,' 'gifting,' 'product seeding,' 'whitelisting creator content,' 'how much to pay an influencer,' or 'FTC disclosure.' For affiliate/referral payout mechanics, see referrals. For community-led advocacy, see community-marketing. For turning creator content into paid ads, see ad-creative.
Explore the method : Influencer MarketingUse when building public-equity-investing initiating coverage reports. Do not use for trade pitches, memos, earnings notes, models, or tearsheets.
Explore the method : Initiating CoverageDevelop an independent visual language and turn the human-chosen direction into a production-ready graphic, brand, campaign, publication, web, or product system. Use when originality and differentiation matter enough to escape generic AI and Big Tech aesthetics. Guides a minimum-evidence start, ten isolated typography-led HTML design directions made only from type, color, and space, visual-first human judgement, explicit commitment, and concrete Round 2 production. Do not use for routine UI fixes, small components, or applying an existing design system.
Explore the method : Innovative UX designGet insider trading activity (SEC Form 4 filings) for one or more stocks. Use when user asks about insider buying/selling, executive transactions, insider sentiment, or Form 4 activity. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Insider TradingAnalyze institutional holdings flows or unusual options activity for a named underlying. Select holdings-based research or the staskh options-whale method explicitly; distinguish reported holdings, observed prints, and inferred positioning.
Explore the method : Institutional Flow TrackerUse when Creative Production is explicitly invoked or mentioned without a concrete request, when the user asks what Creative Production can do, or when the user wants help getting started without enough context to choose a workflow.
Explore the method : IntakeA set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
Explore the method : Internal CommsA set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
Explore the method : Internal communicationsCodified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
Explore the method : Inventory planningUse when drafting or reviewing formal public-equity investment memos. Do not use for live trade construction or credit-first memos; use long-short-pitch or Credit Markets as appropriate.
Explore the method : Investment memoCreate and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.
Explore the method : Investor materialsDraft cold emails, warm intro blurbs, follow-ups, update emails, and investor communications for fundraising. Use when the user wants outreach to angels, VCs, strategic investors, or accelerators and needs concise, personalized, investor-facing messaging.
Explore the method : Investor outreachAccess Jewish calendar data and Shabbat times via Hebcal API. Use when building apps with Shabbat times, Jewish holidays, Hebrew dates, or Zmanim. Triggers on Shabbat times, Hebcal, Jewish calendar, Hebrew date, Zmanim.
Explore the method : Jewish calendar lookupCreate, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable.
Explore the method : Jupyter NotebooksMonitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Kanchi Dividend Review MonitorConvert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Kanchi Dividend SOPProvide US dividend tax and account-location workflow for Kanchi-style income portfolios. Use when users ask about qualified vs ordinary dividends, 1099-DIV interpretation, REIT/BDC distribution treatment, holding-period checks, or taxable-vs-IRA account placement decisions for dividend assets. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Kanchi Dividend US Tax AccountingCapture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.
Explore the method : Knowledge captureBuild monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms. Use when turning metrics into a working dashboard instead of a vanity board.
Explore the method : Kpi designPrepare KPI readouts, scorecards, WBR/MBR/QBR updates, and executive summaries from quantitative business or product metrics; use when the task is to report status, compare against targets, explain validated drivers, and state operating implications.
Explore the method : Kpi reportingPrepare KPI readouts, scorecards, WBR/MBR/QBR updates, and executive summaries from quantitative business or product metrics; use when the task is to report status, compare against targets, explain validated drivers, and state operating implications.
Explore the method : Kpi ReportingAudit, design, or improve a paid-campaign landing page for message match, offer clarity, conversion path, privacy-safe measurement, mobile usability, and next-test decisions. Use when a user asks about ad landing pages, lead-magnet pages, conversion, landing-page copy, or whether a page is ready for paid traffic.
Explore the method : Landing page reviewBuild LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
Explore the method : Langchain developmentWhen the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature release,' 'announcement,' 'go-to-market,' 'beta launch,' 'early access,' 'waitlist,' 'product update,' 'how do I launch this,' 'launch checklist,' 'GTM plan,' or 'we're about to ship.' Use this whenever someone is preparing to release something publicly. For ongoing marketing after launch, see marketing-ideas. For the offer being launched (bonuses, guarantees, scarcity, naming), see offers.
Explore the method : LaunchWhen the user wants to create, plan, or optimize a lead magnet for email capture or lead generation. Also use when the user mentions "lead magnet," "gated content," "content upgrade," "downloadable," "ebook," "cheat sheet," "checklist," "template download," "opt-in," "freebie," "PDF download," "resource library," "content offer," "email capture content," "Notion template," "spreadsheet template," or "what should I give away for emails." Use this for planning what to create and how to distribute it. For interactive tools as lead magnets, see free-tools. For writing the actual content, see copywriting. For the email sequence after capture, see emails.
Explore the method : Lead MagnetsAI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.
Explore the method : Lead qualificationTeach the user a new skill or concept, within this workspace.
Explore the method : Learning coachTeach the user a new skill or concept, within this workspace.
Explore the method : Learning designDocument a recently solved problem to compound your team's knowledge
Explore the method : Lessons learnedImpermanent loss calculation, modeling, and breakeven analysis for AMM liquidity provision across pool types. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Liquidity Provision AnalysisConduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
Explore the method : Literature synthesisLoad large LLMs with 4-bit NF4 quantization and optional double quantization via BitsAndBytes to reduce GPU memory by 4x while preserving inference quality
Explore the method : LLM: 4bit nf4 double quantizationShared-backbone neural network with actor (policy) and critic (value) heads for grid-based game agent RL training
Explore the method : LLM: actor critic game agentDynamically reduce max_tokens and batch size as wall-clock time approaches a cutoff to ensure all inputs get processed
Explore the method : LLM: adaptive time budget inferenceEnsemble multi-model LLM predictions using weighted combination of average probability, cross-model agreement ratio, and max confidence
Explore the method : LLM: agreement confidence ensembleParse sequential yes/no answers to build inclusion/exclusion sets, then apply compound boolean filters to narrow a candidate list
Explore the method : LLM: answer accumulation filterSteers LLM output format by injecting a partial assistant response into the chat template before generation, forcing structured output without fine-tuning.
Explore the method : LLM: assistant prefill response steeringBatch-compute perplexity for multiple texts using a causal LM with proper padding, shifted labels, and pad-token masking for efficient GPU utilization
Explore the method : LLM: batched perplexity scoringForce-clamp free-form LLM output to binary yes/no with keyword matching and a fallback default for constrained environments
Explore the method : LLM: binary answer clampingHierarchical binary search over entity space by asking category, region, then first-letter questions to narrow candidates before guessing
Explore the method : LLM: binary search entity narrowingExtract final numeric answers from LaTeX \boxed{} notation in LLM math reasoning output, scanning matches in reverse for robustness
Explore the method : LLM: boxed answer extractionPer-agent COLLECT/DEPOSIT state machine driven by cargo thresholds with greedy neighbor selection for resource collection games
Explore the method : LLM: cargo threshold state machineExecute Python code blocks from LLM math responses in a sandbox, then double-weight code-derived answers in majority voting
Explore the method : LLM: code execution weighted voteFine-tune an instruct LLM on a single text field containing prompt+answer using TRL's DataCollatorForCompletionOnlyLM, which masks the loss to only the answer tokens by detecting a response template string at collation time
Explore the method : LLM: completion only lm trainingUses primary model predictions only when confidence exceeds a threshold, falling back to a backup ensemble otherwise.
Explore the method : LLM: confidence threshold fallbackTest LLM evaluator robustness against counting trap adversarial essay. Use only labeled adversarial fixtures and authorized test judges; never alter real submissions or falsify assessment evidence.
Explore the method : LLM: counting trap adversarial essaySingle agent entry point that dispatches between multiple roles (ask/answer/guess) based on turn type, combining heuristic and LLM-based strategies
Explore the method : LLM: dual role agent dispatchEnumerate all factorial permutations in batches with LLM scoring, tracking the running best and early-stopping when score crosses a known optimality threshold
Explore the method : LLM: exhaustive permutation early stoppingTest LLM evaluator robustness against few shot conversation injection. Use only labeled adversarial fixtures and authorized test judges; never alter real submissions or falsify assessment evidence.
Explore the method : LLM: few shot conversation injectionGreedy local search that removes one element from a fixed position and re-inserts it at every possible index, keeping the best improvement per round
Explore the method : LLM: greedy word reinsert searchStructured prompt template for recovering the instruction that transformed one text into another, with labeled original/rewritten fields and explicit task framing
Explore the method : LLM: inverse task prompt templateNarrows a large candidate pool through multiple LLM voting rounds, each presenting a sliding window of candidates plus the current best pick.
Explore the method : LLM: iterative candidate rerankingIteratively prompts an LLM over document pairs to extract and deduplicate keywords, building a comprehensive term set from multiple perspectives.
Explore the method : LLM: iterative pairwise keyword extractionShares KV cache from a common prefix (context+question) across multiple answer suffixes for efficient multi-choice scoring.
Explore the method : LLM: kv cache prefix scoringScore a binary classification prompt by reading the logits of the True/False (or Yes/No) token IDs at the final position and softmaxing only those two values, skipping generation entirely for a 10-50x speedup over decoding
Explore the method : LLM: last token logit binary scoringExtracts dense sentence embeddings from decoder-only LLMs by pooling the last non-padding token's hidden state.
Explore the method : LLM: last token pooling embeddingLoad a pretrained LLM with LoRA adapter via PEFT for memory-efficient fine-tuned sequence classification
Explore the method : LLM: lora sequence classificationTest LLM evaluator robustness against low meaning input substitution. Use only labeled adversarial fixtures and authorized test judges; never alter real submissions or falsify assessment evidence.
Explore the method : LLM: low meaning input substitutionRun independent vLLM workers on each GPU by spawning one mp.Process per device and setting CUDA_VISIBLE_DEVICES inside the child before vLLM is imported, sidestepping vLLM's single-instance-per-process limitation
Explore the method : LLM: multi gpu process isolated vllmConstrains LLM generation to a fixed set of valid choice tokens using a logits processor for structured single-token output.
Explore the method : LLM: multiple choice logits processorRank candidate prompts by computing LLM perplexity of the full conversation conditioned on each prompt, selecting the lowest-perplexity candidate as the best match
Explore the method : LLM: perplexity prompt rankingGenerate multiple LLM responses using diverse system prompt variants to increase reasoning diversity for self-consistency voting
Explore the method : LLM: prompt variant ensembleTest LLM evaluator robustness against pseudo metadata score injection. Use only labeled adversarial fixtures and authorized test judges; never alter real submissions or falsify assessment evidence.
Explore the method : LLM: pseudo metadata score injectionTest LLM evaluator robustness against score variance baseline mixin. Use only labeled adversarial fixtures and authorized test judges; never alter real submissions or falsify assessment evidence.
Explore the method : LLM: score variance baseline mixinAggregate multiple LLM reasoning attempts via majority voting with random jitter tiebreaking and validity filtering
Explore the method : LLM: self consistency majority voteTruncate LLM output to exactly N sentences and fall back to a known-good baseline string when output is empty or too short
Explore the method : LLM: sentence truncation fallbackLocal search that slides a window of size p across a word sequence, brute-forcing all permutations within each window to minimize an objective like LLM perplexity
Explore the method : LLM: sliding window permutation searchAgents patrol in expanding spiral patterns using rotating direction sequences with increasing radius for systematic grid exploration
Explore the method : LLM: spiral patrol explorationInitialize text ordering by placing stopwords first then content words, producing low-perplexity starting points for combinatorial search over word permutations
Explore the method : LLM: stopword priority sortingParses LLM-generated markup (SVG, HTML, XML) with lxml, strips disallowed elements and attributes via an allowlist, and validates structural constraints like path data.
Explore the method : LLM: structured output sanitizationPrompts an LLM to generate valid SVG by embedding an explicit element/attribute allowlist and a one-shot example, then extracts the last valid SVG block from output.
Explore the method : LLM: svg constrained generationAssign each new agent a different directional rotation pattern from a set of permutations to ensure swarm coverage diversity across the map
Explore the method : LLM: swarm tactic diversityGenerates additional training examples using a stronger LLM (e.g., GPT-3.5) to augment small labeled datasets.
Explore the method : LLM: synthetic data augmentationConvert a test.csv with paired positive/negative example columns into a labeled training set at inference time, using the OTHER example as the in-prompt demonstration so the model never sees its own target as a few-shot exemplar
Explore the method : LLM: test time train pseudo label expansionScalable TF-IDF retrieval over large document corpora using frozen vocabulary and chunked top-k merging.
Explore the method : LLM: tfidf chunked retrievalRun multiple LLM inference jobs in parallel using Python threads, each pinned to a separate GPU with staggered starts
Explore the method : LLM: threaded multi gpu inferenceRebuild multi-turn conversation context by interleaving user/assistant turns with chat template tokens into a single prompt each call
Explore the method : LLM: turn based prompt accumulationRefines retrieval by running two passes: initial embedding retrieval to get candidates, then LLM-generated text concatenated with the query for a second retrieval pass.
Explore the method : LLM: two pass retrieval refinementServe a quantized base LLM with a hot-swappable LoRA adapter under vLLM, enabling prefix caching and tensor parallelism so a single fine-tuned adapter runs at production throughput without merging weights
Explore the method : LLM: vllm lora adapter inferenceTest LLM evaluator robustness against weighted attack strategy sampling. Use only labeled adversarial fixtures and authorized test judges; never alter real submissions or falsify assessment evidence.
Explore the method : LLM: weighted attack strategy samplingDense retrieval over a FAISS-indexed Wikipedia corpus to provide grounding context for LLM question answering.
Explore the method : LLM: wikipedia rag retrievalRoute AI coding queries to local LLMs in air-gapped networks. Integrates Serena MCP for semantic code understanding. Use when working offline, with local models (Ollama, LM Studio, Jan, OpenWebUI), or in secure/closed environments. Triggers on local LLM, Ollama, LM Studio, Jan, air-gapped, offline AI, Serena, local inference, closed network, model routing, defense network, secure coding.
Explore the method : Local model routingCodified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience. Includes escalation protocols, carrier-specific behaviors, claims procedures, and judgment frameworks. Use when handling shipping exceptions, freight claims, delivery issues, or carrier disputes.
Explore the method : Logistics exception planningUse when building PM-facing Public Equity Investing trade pitches, including sparse-context or partial-section requests. Do not use for formal memos; use memo-builder.
Explore the method : Long Short PitchDetect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Macro Regime DetectorOne page, hands-on manual tier — drive a live page through the web accessibility (a11y) checks a rule engine can't decide: keyboard operation and focus order, screen-reader names, roles and states from the accessibility tree, reflow and zoom, reduced motion, form errors, and target size. Grades each finding by evidence basis (verified / confirm-with-a-human / human-required) and severity, and closes every criterion in a ledger: verified, flagged, not exercised, or N/A. Locates and assesses; does not fix (use `accessibility-fix`). Use it for keyboard testing, focus-order checks, screen-reader or a11y-tree review, reflow and zoom at 200%, or 'is this operable, not just lint-clean'. The automated tier is `accessibility-scan`; `accessibility-audit` runs both across a sampled site.
Explore the method : Manual accessibility inspectionIdentify and compare adjacent markets using three horizons, customer/capability/channel vectors, transferable assets, budget competition, and explicit entry conditions.
Explore the method : Market adjacency analysisResearch a market, including sizing, target segments, competitive landscape, pricing power, differentiation hypotheses, and confidence. Use for market research, TAM/SAM/SOM, competitive landscape, pricing power, or market-entry questions. Produces an evidence-backed internal Markdown report and optional inventory update.
Explore the method : Market analysisAssess market breadth from public CSV observations or supplied breadth/uptrend charts. Select the quantitative scoring or chart-reading method for the available evidence. Distinguish breadth measurements, uptrend participation, observation dates, and the exposure decision they may inform.
Explore the method : Market breadth analysisMarket data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Market Data PreparationComprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Market Environment AnalysisDEX orderflow analysis, trade classification, buyer/seller pressure, and microstructure signals for Solana tokens. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Market Microstructure AnalysisAnalyze financial-market news and catalysts or retrieve company headlines and sentiment. Choose broad market synthesis or the company-specific staskh adapter; preserve publication times, source evidence, and uncertainty about price impact.
Explore the method : Market News AnalystAssess risk metrics for a stock or position including volatility, beta, VaR, and drawdown analysis. Use when user asks about risk, volatility, beta, VaR, value at risk, drawdown, or position sizing. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Market Risk AssessmentEstimate market size and reachable opportunity with transparent TAM, SAM, SOM, or equivalent models. Use for market sizing, addressable market, revenue pool, opportunity sizing, capacity-constrained opportunity, or investment and entry decisions. Produces an evidence-backed estimate with assumptions and confidence; it does not present a model as measured fact.
Explore the method : Market sizingEstimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities.
Explore the method : Market SizingDetects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Market Top DetectorWhen the user wants multiple expert perspectives on a marketing question — a simulated board of advisors staffed by legendary marketers (Seth Godin, David Ogilvy, Eugene Schwartz, April Dunford, Rory Sutherland, Alex Hormozi, Byron Sharp, and more). Also use when the user mentions 'marketing council,' 'board of advisors,' 'advisory board,' 'what would Seth Godin say,' 'what would Ogilvy think,' 'channel Hormozi,' 'get multiple perspectives,' 'debate this,' 'have the council review,' 'marketing mentors,' or asks how a famous marketer would approach their problem. The council gives each advisor's take through their documented frameworks, surfaces where they disagree, and synthesizes a recommendation. For executing the winning direction, hand off to positioning, offers, copywriting, ads, or the relevant skill.
Explore the method : Marketing CouncilMaintain an evidence-bounded marketing learning loop across content, campaigns, ads, websites, and funnels. Use when a user needs to review results, record a learning, decide whether to continue, change, stop, fix measurement, or connect marketing activity to financial proof.
Explore the method : Marketing experiment reviewGenerate and prioritize marketing ideas, growth tactics, and promotional strategy for SaaS, software, AI, and service businesses. Use whenever the user asks for marketing ideas, growth ideas, how to market something, marketing strategies, tactics, ways to promote, brainstorm marketing, what else to try, or what marketing should I do. Start here when the user is stuck and needs practical inspiration; name an appropriate channel-specific workflow when execution or proof needs specialist support.
Explore the method : Marketing ideasWhen the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.
Explore the method : Marketing LoopsWhen the user needs a comprehensive marketing plan for a client, a company they advise, or their own product. Also use when the user mentions "marketing plan," "growth plan," "GTM plan," "go-to-market plan," "AARRR plan," "90-day marketing plan," "12-month marketing roadmap," "fractional CMO plan," "fCMO plan," "market sizing," "TAM SAM SOM," "how big is this market," or "market research." Generates an exhaustive 13-section plan structured by AARRR (Acquisition, Activation, Retention, Referral, Revenue), customized to the client's current budget, team, and stage, mapped to future funding milestones, cross-referenced with the 139-idea marketing-ideas library and an embedded 17-section current-state audit rubric, with a marketing operations stack showing which skills and MCP/API integrations execute each part. Outputs a Notion-paste-ready markdown document. For positioning and ICP context before planning, see product-marketing. For stage-specific deep work, see onboarding, signup, emails, referrals, pricing.
Explore the method : Marketing PlanApply psychological principles, mental models, behavioral science, persuasion, cognitive bias, buyer decision-making, pricing psychology, social proof, scarcity, loss aversion, anchoring, framing, nudges, and consumer behavior to marketing. Use when the user wants to understand why people buy or ethically improve messaging, offers, pages, pricing, funnels, or campaigns using psychology.
Explore the method : Marketing psychologyEvaluate marketing return using attributable results, funnel economics, incrementality, and clearly labeled assumptions. Use when a user asks to prove, model, forecast, audit, or report ROI for campaigns, paid media, websites, content, or lead-generation work.
Explore the method : Marketing roi analysisMean-reversion strategy tools including Hurst exponent, half-life estimation, z-score signals, ADF testing, and Ornstein-Uhlenbeck modeling. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Mean Reversion AnalysisPlan and quality-check marketing measurement for web, paid media, forms, CRM handoff, and offline outcomes. Use when a user asks about analytics, conversion events, pixels, tags, UTMs, source capture, attribution readiness, or how to verify campaign tracking.
Explore the method : Measurement planningTurn a completed meeting into evidence-backed commitments, proposed record updates, and reviewable follow-up drafts. Use after a call or meeting when the user needs a recap, next steps, ownership, or a draft message. This method never sends messages or changes records.
Explore the method : Meeting followupUse when creating Public Equity Investing meeting prep briefs. Do not use for private diligence, IB, FP&A, legal, or scheduling-only tasks.
Explore the method : Meeting PrepPrepare an evidence-backed brief for one upcoming meeting or conversation. Use when a user needs meeting context, the intended decision, relevant history, questions, commitments to check, or open questions. This method is read-only and does not change calendar events or records.
Explore the method : Meeting preparationUse when drafting or reviewing formal public-equity investment memos. Do not use for live trade construction or credit-first memos; use long-short-pitch or Credit Markets as appropriate.
Explore the method : Memo BuilderCreate diagrams and visualizations using Mermaid syntax. Use when generating flowcharts, sequence diagrams, class diagrams, entity-relationship diagrams, Gantt charts, or any visual documentation. Triggers on Mermaid, flowchart, sequence diagram, class diagram, ER diagram, Gantt chart, diagram, visualization.
Explore the method : Mermaid diagrammingPull, analyze, manage, and CREATE Meta ads (Facebook, Instagram, Messenger, Threads, Click-to-WhatsApp) via the Marketing API. Use when the user mentions Meta/FB/IG ads, Ads Manager, ROAS, CPA, CPM, CTR, ad spend, campaign or ad set performance, creative fatigue, frequency, CTWA, audience/placement breakdowns, A/B ad tests, or wants to pause an ad, change a budget, duplicate a winner, or launch a new campaign from scratch. Trigger on Hebrew terms like פרסום בפייסבוק/באינסטגרם, פרסומות מטא, ביצועי קמפיין, פרסומות, צור קמפיין, השק קמפיין, A/B טסט. Trigger on questions like how are my ads doing, which creative is winning, is my ad fatigued, what's my CPA, drop a 30-day report, launch an A/B test for my course, build a campaign with these 3 angles. Trigger even if auth isn't set up — the skill walks through one-time setup. Do NOT use for.
Explore the method : Meta ads operationsDiagnose why a metric changed or differs from expectation. Use when the task is to identify likely drivers of a metric movement, anomaly, gap, or discrepancy.
Explore the method : Metric diagnosticsDiagnose why a metric changed or differs from expectation. Use when the task is to identify likely drivers of a metric movement, anomaly, gap, or discrepancy.
Explore the method : Metric DiagnosticsCreate, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.
Explore the method : Metric dictionaryMEV exposure assessment, sandwich attack detection, and protection strategies for Solana DEX trading. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Mev Risk AnalysisMigrates React projects and components from Radix UI to Base UI. Use when asked to migrate from radix, move to base-ui, convert radix primitives, or switch a shadcn project's base library. Handles single components ("migrate accordion") and whole projects.
Explore the method : Migrate Radix UI to Base UIUse when building public-equity-investing catalyst calendars. Do not use for full event underwriting; use event-driven-analyzer.
Explore the method : Milestone risk reviewUse when auditing existing Public Equity Investing models or spreadsheets. Do not use to build a new model from scratch.
Explore the method : Model Audit TieoutWork with MongoDB databases using best practices. Use when designing schemas, writing queries, building aggregation pipelines, or optimizing performance. Triggers on MongoDB, Mongoose, NoSQL, aggregation pipeline, document database, MongoDB Atlas.
Explore the method : Mongodb developmentGenerate professional fashion/product advertising images using ComfyDeploy's Morpheus Fashion Design workflow. Use when the user asks to create fashion ads, product campaigns, commercial photography with models, or branded content. Supports model face consistency, product integration, and professional camera/lighting simulation.
Explore the method : Morpheus fashion campaign imagerySelect the best MetaTrader 5 trading robots (Expert Advisors) that have not been backtested yet, by running the MT5 Strategy Tester from the command line through a 3-round pipeline. Use when the user wants to batch-test MT5 bots/EAs, screen robots across all symbols, optimize EA parameters, or move candidate bots to finalists based on profit, drawdown, positive months/years and equity-curve criteria. Runs terminal64.exe headless; Windows + MetaTrader 5 required at run time. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : MT5 Robot TesterShape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
Explore the method : Narrative writingJudge whether a market FAILED to react to news favorable to a crowded speculative position — step 2 of Jason Shapiro's COT contrarian process. Consumes a cot-contrarian-detector report (or an explicit direction) plus a Claude-curated events JSON, fetches the underlying price series with a documented fallback chain, and produces a fail-closed CONFIRMED / NOT_CONFIRMED / INSUFFICIENT_EVIDENCE verdict using a statistically validated drift-significance test (not a naive failure-ratio, which false-confirms on pure noise). Generic beyond COT — reusable for PEAD and macro-crowding news-failure checks. Use when the user asks to check news-failure confirmation, whether a crowded market "shrugged off" good/bad news, or wants to run Shapiro step 2 on a CROWDED_LONG/CROWDED_SHORT market. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : News Reaction Failure AnalyzerUses bitsandbytes 8-bit AdamW to halve optimizer memory, with a 32-bit override for embedding weights to prevent instability.
Explore the method : NLP: 8bit optimizer embedding overrideUse Adafactor optimizer with label smoothing for seq2seq fine-tuning — memory-efficient and regularizes overconfident predictions
Explore the method : NLP: adafactor label smoothing seq2seqMerges nearby predicted NER spans of the same class within a word-distance threshold into single coherent segments.
Explore the method : NLP: adjacent span mergingSplit validation by unique anchor/query entities so no anchor appears in both train and val, preventing data leakage in pairwise matching tasks
Explore the method : NLP: anchor grouped validationLearns attention weights over token positions to compute a weighted average of hidden states for sequence representation.
Explore the method : NLP: attention head poolingTrain main target alongside auxiliary sub-type targets as multiple output heads to regularize and improve generalization
Explore the method : NLP: auxiliary target multitaskElement-wise average of multiple pretrained embedding matrices as a parameter-free meta-embedding
Explore the method : NLP: averaged meta embeddingWhen no candidate passes the threshold for a query, fall back to the single highest-scoring match to guarantee at least one prediction per query
Explore the method : NLP: best prob fallback matchingCombine subgroup AUC, BPSN AUC, and BNSP AUC across identity groups via power-mean weighting with overall AUC for fairness evaluation
Explore the method : NLP: bias aware auc metricDouble training data by adding reverse-direction translation pairs with task prefix prompts
Explore the method : NLP: bidirectional translation augmentationReconstructs named entity spans from BIO token-level tags, handling B/I/O transitions and sentence boundaries.
Explore the method : NLP: bio tag span reconstructionSplits long documents into overlapping fixed-length windows with BIO NER tags for BERT token classification on sequences exceeding max length.
Explore the method : NLP: bio tagging sliding windowReconstruct character-level offsets for BPE tokens by decoding each token individually and accumulating lengths for precise span mapping
Explore the method : NLP: bpe offset char alignmentGroup variable-length sequences into length-sorted buckets before batching to minimize padding waste during seq2seq inference
Explore the method : NLP: bucket batching seq2seqConverts multi-label binary flags into a continuous regression target by applying hand-tuned per-category multipliers, then averaging across categories.
Explore the method : NLP: category weighted score fusionBlend character-level probability arrays from multiple models with OOF-tuned weights before thresholding
Explore the method : NLP: char prob weighted blendAverage predictions from each epoch checkpoint with exponentially increasing weights (2^epoch), favoring later more-converged snapshots
Explore the method : NLP: checkpoint ensemble exponentialGeometric mean of chrF and BLEU as a balanced composite translation evaluation metric
Explore the method : NLP: chrf bleu geometric mean metricMerges multiple training datasets while keeping all positive examples and downsampling negatives to control class imbalance.
Explore the method : NLP: class balanced dataset mergeReranks candidate answer spans by subtracting the CLS token's start+end logit sum as a null-answer baseline score.
Explore the method : NLP: cls null score span rerankingResolves mixed cp1252/utf-8 encoding artifacts in text via round-trip encode/decode with custom error handlers and unidecode normalization.
Explore the method : NLP: cp1252 utf8 encoding normalizationMerge candidate pools from multiple independent seq2seq models before MBR selection to reduce shared failure modes
Explore the method : NLP: cross model candidate poolTrains a Byte-Pair Encoding tokenizer on the task corpus to capture domain-specific vocabulary, typos, and subword patterns.
Explore the method : NLP: custom bpe tokenizerCyclical learning rate (CLR) Keras callback that oscillates LR between base and max each batch for faster convergence
Explore the method : NLP: cyclical lr triangularFine-tunes DeBERTa-v3 for text classification tasks. Use when building text classifiers, sentiment analysis, or multi-label classification on domain-specific data.
Explore the method : NLP: deberta classificationAdd domain-specific categorical values as new special tokens, resize embeddings, and prepend them to input so the model learns domain-aware representations
Explore the method : NLP: domain special token embeddingTrains a WordPiece tokenizer on in-domain text, then feeds its subword token IDs into TF-IDF vectorization for domain-adapted sparse features.
Explore the method : NLP: domain wordpiece tfidfExplicitly zero all dropout probabilities in transformer config at load time for fully deterministic inference
Explore the method : NLP: dropout disabled inferenceConcatenate GloVe and FastText embedding matrices along feature axis for richer 600d word representations
Explore the method : NLP: dual embedding concatPads each batch to its actual max sequence length instead of the global max_len, reducing wasted computation.
Explore the method : NLP: dynamic batch paddingUse pretrained embedding vocab to decide which punctuation to keep, split, or remove before tokenization
Explore the method : NLP: embedding aware punct normalizationMeasure pretrained embedding coverage over dataset vocab and return OOV words sorted by frequency for targeted preprocessing
Explore the method : NLP: embedding coverage analysisInitialize out-of-vocabulary word embeddings with Gaussian noise matching the pretrained embedding distribution
Explore the method : NLP: embedding matrix oov gaussian initUse separate learning rates for pretrained backbone (low) and randomly initialized classification head (high)
Explore the method : NLP: encoder decoder lr splitUses a fine-tuned masked language model to classify candidate entity spans by comparing fill-mask probabilities for positive vs negative marker tokens.
Explore the method : NLP: fill mask entity classificationGPU-accelerated TF-IDF vectorization via RAPIDS cuML with chunked cosine similarity for large-scale text matching
Explore the method : NLP: gpu tfidf cosine matchingTrades compute for memory by recomputing intermediate activations during backprop instead of storing them, reducing memory from O(n) to O(sqrt(n)).
Explore the method : NLP: gradient checkpointingPreserves both the start and end of long text sequences when truncating for transformer input limits.
Explore the method : NLP: head tail truncationConcatenate multi-level categorical fields into a compound label (Category:Subcategory) for flat multiclass classification
Explore the method : NLP: hierarchical label encodingWeight BCE loss by identity subgroup membership to debias predictions — upweight samples where identity conflicts with label
Explore the method : NLP: identity weighted bce lossFurther pretrains a transformer with masked language modeling on the target task's own text before fine-tuning.
Explore the method : NLP: in task pretrainingDeduplicates extracted entity predictions by filtering out candidates whose Jaccard word-overlap with already-accepted labels exceeds a threshold.
Explore the method : NLP: jaccard dedup prediction filterComputes micro F-beta for NER where true positives are determined by Jaccard word-overlap >= threshold rather than exact string match.
Explore the method : NLP: jaccard fbeta ner metricConverts Japanese scripts (Hiragana, Katakana, Kanji) to romanized ASCII using pykakasi for cross-script entity matching.
Explore the method : NLP: japanese transliteration normalizationDual-head transformer model that jointly predicts start/end span logits from sequence output and answer type from pooled CLS output.
Explore the method : NLP: joint span answer type headEvaluates predicted sequence ordering quality using Kendall Tau correlation via efficient O(n log n) inversion counting.
Explore the method : NLP: kendall tau ordering metricReplay keystroke activity logs (Input/Replace/Paste/Remove/Move) against a string buffer to reconstruct the evolving essay text
Explore the method : NLP: keystroke essay reconstructionApplies label smoothing to binary cross-entropy loss to reduce overconfidence and improve generalization in text classification.
Explore the method : NLP: label smoothing binaryConcatenate the last two transformer hidden states along the feature dimension before the task head for richer token representations
Explore the method : NLP: last hidden states concatApplies different learning rates to transformer encoder vs task-specific head, with no weight decay on bias and LayerNorm.
Explore the method : NLP: layerwise lr decayLatent Dirichlet Allocation on CountVectorizer bag-of-words to discover latent topics with per-document topic distributions for feature engineering or EDA
Explore the method : NLP: lda topic modelingReplace mean pooling with a trainable attention network (Linear-Tanh-Linear-Softmax) that learns token importance weights over transformer hidden states
Explore the method : NLP: learned attention poolingSort texts by length before batching with dynamic padding to minimize wasted padding tokens and speed up transformer inference
Explore the method : NLP: length sorted batchingFeeds CLS token embeddings from each transformer layer into a BiLSTM to learn an optimal combination across layer depth.
Explore the method : NLP: lstm over transformer layersCustom MAP@K evaluation metric — scores top-K classification predictions with reciprocal rank weighting for HuggingFace Trainer
Explore the method : NLP: map at k metricBinary cross-entropy loss with mask to ignore special and padding tokens in token-level span classification
Explore the method : NLP: masked bce span lossMinimum Bayes Risk decoding — select the candidate with highest average chrF++ agreement against all others in the pool
Explore the method : NLP: mbr decoding rerankingConcatenates token-level mean pooling and max pooling from the last hidden state for a richer sequence representation.
Explore the method : NLP: mean max concat poolingComputes attention-mask-weighted mean of token embeddings, excluding padding tokens from the average.
Explore the method : NLP: mean pooling with maskMin-max normalizes each model's predictions to [0,1] before averaging, ensuring equal contribution regardless of score distribution scale.
Explore the method : NLP: minmax normalized ensemble blendComputes multiple complementary string similarity scores (Gestalt, Levenshtein, Jaro-Winkler, LCS) per field pair as features for entity matching classifiers.
Explore the method : NLP: multi metric string similarityRun multiple independent retrieve-rerank pipelines and union-merge their predicted IDs per query via explode-groupby-unique
Explore the method : NLP: multi retriever union ensembleGenerate diverse translation candidates by running nucleus sampling at multiple temperatures then pooling for MBR selection
Explore the method : NLP: multi temperature candidate samplingApplies multiple dropout masks to the same hidden state and averages predictions for regularization and variance reduction.
Explore the method : NLP: multisample dropoutDownsamples documents with no entity labels while keeping all positive samples, balancing class distribution in NER training without discarding entity-bearing examples.
Explore the method : NLP: negative sample downsamplingOverride model span predictions with full text for neutral sentiment or very short inputs where sub-span extraction is unreliable
Explore the method : NLP: neutral short text fallbackThresholds the O-class (non-entity) softmax probability in NER: if below threshold, overrides with the best non-O class to boost entity recall.
Explore the method : NLP: o class threshold suppressionExports a HuggingFace transformer to ONNX with dynamic axes, then auto-converts to BF16 mixed precision for 30-200% GPU inference speedup with 2x memory reduction.
Explore the method : NLP: onnx mixed precision exportLearns optimal per-model blending weights via Optuna optimization, supporting negative weights for error cancellation.
Explore the method : NLP: optuna ensemble weightsTrains a transformer with MarginRankingLoss on text pairs (more/less toxic), learning to rank rather than classify when only pairwise preference labels are available.
Explore the method : NLP: pairwise margin ranking lossEvaluates ranking models by computing the fraction of preference pairs where the model correctly scores the preferred item higher.
Explore the method : NLP: pairwise ranking validationConverts pairwise binary predictions into continuous ranks via temperature-scaled softmax weighted sum over anchor positions.
Explore the method : NLP: pairwise softmax rank aggregationUse Pearson correlation coefficient as evaluation metric for semantic similarity regression tasks, selecting best checkpoint by correlation rather than loss
Explore the method : NLP: pearson correlation metricFilters predicted NER spans using per-class minimum word-count and mean-probability thresholds to reduce false positives.
Explore the method : NLP: per class span filteringPrepends prompt question or full prompt text to input with SEP token for context-aware text evaluation.
Explore the method : NLP: prompt text concatenationStores model softmax probabilities as uint8 (0-255) to reduce RAM by 4x during multi-model ensemble inference.
Explore the method : NLP: quantized probability storageExtracts established readability scores (Flesch, Gunning FOG, ARI, Coleman-Liau) as numeric features from text.
Explore the method : NLP: readability metric featuresSupplements transformer NER predictions with regex-based detection for structured entities (email, phone, URL), aligning regex matches back to token indices via subsequence search.
Explore the method : NLP: regex hybrid ner fallbackSplit multi-sentence parallel pairs into aligned sentence pairs to expand training data for seq2seq models
Explore the method : NLP: sentence alignment augmentationPrepend a sentiment token as the query in a QA-style input to condition span extraction on sentiment class without architectural changes
Explore the method : NLP: sentiment conditioned qa spanPromotes a predicted short answer span to its enclosing long-answer candidate by matching token boundaries against pre-extracted document structure.
Explore the method : NLP: short to long span promotionUses simulated annealing to select the optimal subset of candidate terms for a boolean search query, maximizing a retrieval metric like AP@K.
Explore the method : NLP: simulated annealing query optimizationStratifies CV folds by both target label AND data source to prevent source-specific bias in each fold.
Explore the method : NLP: source balanced stratified foldClean up character-level span predictions by removing isolated space characters at span boundaries
Explore the method : NLP: space aware span postprocessingTrain per-class spaCy NER models to extract task-specific spans as custom named entities with compounding batch sizes
Explore the method : NLP: spacy custom ner span extractionEvaluates NER span predictions using bidirectional word-index overlap (>=50% both ways) to compute micro-F1 over predicted vs ground-truth spans.
Explore the method : NLP: span overlap f1 metricDrop entire embedding channels consistently across all timesteps — preserves temporal structure better than element-wise dropout
Explore the method : NLP: spatial dropout embeddingCustom Keras callback that evaluates Spearman correlation on validation data each epoch with optional early stopping.
Explore the method : NLP: spearman correlation callbackApplies domain-aware spelling correction before transformer input to separate spelling errors from content quality.
Explore the method : NLP: spell correction preprocessingUses square root of MSE as training loss to directly optimize for RMSE evaluation metric alignment.
Explore the method : NLP: sqrt mse lossFormat multi-field tabular data into a structured natural language prompt with labeled sections for encoder or LLM classification
Explore the method : NLP: structured prompt formattingControls whether all subtokens or only the first subtoken of each word receive NER labels during training and inference.
Explore the method : NLP: subtoken labeling strategyEnrich model input by mapping categorical codes to human-readable taxonomy descriptions and concatenating them as context for transformer models
Explore the method : NLP: taxonomy context enrichmentFits TF-IDF vectorizer on test set first to extract vocabulary, then retrains on train set using that vocabulary for feature consistency.
Explore the method : NLP: test vocabulary alignmentComputes n-gram overlap counts/ratios and NER entity overlap between reference and generated text as features.
Explore the method : NLP: text overlap featuresHigh n-gram TF-IDF (3-5 grams) with sublinear TF feeding into a weighted soft-voting ensemble of traditional ML classifiers.
Explore the method : NLP: tfidf ngram classifierEncodes text pairs by computing the absolute difference of their TF-IDF vectors, collapsing a pair into a single fixed-length feature vector.
Explore the method : NLP: tfidf pair difference encodingConverts TF-IDF top-k terms into field-scoped boolean OR queries for structured document retrieval from a full-text index.
Explore the method : NLP: tfidf to boolean queryTF-IDF similarity retrieval from a translation memory with SequenceMatcher reranking as a fallback or ensemble component
Explore the method : NLP: tfidf translation memoryComputes word overlap ratio between two texts weighted by inverse corpus frequency, giving rare shared words more importance than common ones.
Explore the method : NLP: tfidf weighted word matchFilters candidate span indices through a token map to skip special tokens, then cross-products top-k start/end indices with length constraints.
Explore the method : NLP: token map filtered topk spansMap token-level classifier outputs back to character-level spans via offset mapping, thresholding, and contiguous grouping
Explore the method : NLP: token to char span predictionAuto-detects TPU vs CPU/GPU at runtime and wraps model construction in the appropriate TensorFlow distribution strategy with scaled batch size.
Explore the method : NLP: tpu distribution strategyDistributes inference across multiple TPU cores using torch_xla, each core writing a CSV shard, then merges shards via groupby mean.
Explore the method : NLP: tpu multicore inferenceTrains a transformer at shorter sequence length for speed, then runs inference at a longer sequence length to capture more context, exploiting position embedding generalization.
Explore the method : NLP: train short infer long sequenceExtracts frozen embeddings from multiple pretrained transformers and trains SVR on the concatenated features.
Explore the method : NLP: transformer embedding svrFreezes transformer embedding and lower encoder layers to reduce memory, speed up training, and stabilize fine-tuning.
Explore the method : NLP: transformer layer freezingTwo-stage stacking: transformer predictions plus hand-crafted features feed into a LightGBM meta-learner.
Explore the method : NLP: transformer lgbm stackingMulti-rule regex pipeline to clean seq2seq translation outputs — deduplicate phrases, fix punctuation, remove artifacts
Explore the method : NLP: translation regex postprocessingFine-tunes a bi-encoder with triplet loss using retrieval-mined hard negatives for dense similarity search.
Explore the method : NLP: triplet loss biencoder finetuningTwo-stage pipeline where an unsupervised bi-encoder retrieves KNN candidates and a supervised cross-encoder reranks them with sigmoid thresholding
Explore the method : NLP: two stage retrieve rerankSamples N items uniformly by stride from a variable-length list, always preserving the first and last elements, to fit long sequences into a fixed token budget.
Explore the method : NLP: uniform stride samplingAfter training on the primary split, continues fine-tuning on the validation set to adapt the model to the target distribution before inference.
Explore the method : NLP: validation finetune continuationExclude bias terms and LayerNorm weights from weight decay to prevent regularization from distorting normalization layers
Explore the method : NLP: weight decay bias exclusionLearns a weighted combination of CLS embeddings across all transformer layers instead of using only the last layer.
Explore the method : NLP: weighted layer poolingBuilds a Whoosh full-text search index over documents and queries it with boolean operators, field scoping, and proximity matching.
Explore the method : NLP: whoosh fulltext search indexUses Word2Vec vocabulary rank as a word frequency proxy for Norvig-style spell correction, avoiding the need for a separate frequency corpus.
Explore the method : NLP: word2vec spell correctionStratifies CV folds by annotator/worker ID to prevent annotator style leakage across train and validation splits in crowd-sourced datasets.
Explore the method : NLP: worker stratified kfoldRoutes QA predictions through an answer-type classifier to emit boolean answers, extractive spans, or null based on type logits.
Explore the method : NLP: yes no answer type routingCapture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.
Explore the method : Notion Knowledge CapturePrepare meeting materials with Notion context and supplemental research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees.
Explore the method : Notion Meeting IntelligenceResearch across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
Explore the method : Notion Research DocumentationTurn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.
Explore the method : Notion Spec To ImplementationWhen the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.
Explore the method : OffersWhen the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.
Explore the method : OnboardingCoordinated wallet cluster detection, wash trading identification, and fake activity analysis for Solana tokens. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Onchain Wallet AnalysisReview a stated operating period and recommend evidence-backed improvements. Use for weekly or periodic retrospectives, outcome reviews, learning reviews, or decisions about what to continue, change, stop, or investigate. This method does not change records, budgets, campaigns, or other live systems.
Explore the method : Operating retrospectiveGenerate and critically evaluate grounded improvement ideas for the current project. Use when asking what to improve, requesting idea generation, exploring surprising improvements, or wanting the AI to proactively suggest strong project directions before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on this project', 'surprise me with improvements', 'what would you change', or any request for AI-generated project improvement suggestions rather than refining the user's own idea.
Explore the method : Opportunity ideationRetrieve and inspect option chains, expiries, bid/ask, liquidity, implied volatility, and available Greeks. Select the staskh market-data adapter or explicitly requested IBKR source, including futures options. Requires a real data connection; does not construct or execute a strategy by itself.
Explore the method : Option ChainCompare options strategies or analyze multi-leg spreads, including verticals, diagonals, iron condors, straddles, and strangles. Inspect Greeks, liquidity, exercise/assignment, payoff, and downside using available evidence; distinguish modeled payoff from executable quotes.
Explore the method : Options Strategy AdvisorDraft evidence-based cold outreach, follow-up messages, and phone-first talk tracks for a supplied prospect or account list. Use when a user needs personalized sales outreach, contact prioritization, a draft-only email, or an outreach review. Never sends or contacts anyone.
Explore the method : Outreach writingAudit paid advertising performance, tracking, landing pages, creative, audience, budget, and funnel conversion. Use when a business needs an ads health check, PPC or paid-media audit, campaign review, ROAS or CAC diagnosis, tracking review, or prioritized improvements to leads, sales, or pipeline.
Explore the method : Paid ad performance reviewPlan and improve accountable paid-media campaigns across search, social, video, and marketplace platforms. Use when a user needs campaign strategy, launch readiness, targeting, budget guardrails, retargeting, bidding, CPA or ROAS diagnosis, reporting, or optimization recommendations.
Explore the method : Paid media planningStatistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Pair Trade ScreenerScreen US equities for parabolic exhaustion patterns and generate conditional pre-market short plans, then evaluate intraday trigger fires from live 5-min bars. Phase 1 daily 5-factor scorer (MA extension / acceleration / volume climax / range expansion / liquidity), Phase 2 per-candidate plans for ORL break / first-red 5-min / VWAP fail with explicit borrow / SSR / manual-confirmation gating, Phase 3 one-shot intraday FSM that detects trigger fires and resolves concrete share counts. Covers Phase 1 + Phase 2 + Phase 3. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Parabolic Short Trade PlannerWhen the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.
Explore the method : PaywallsIterative PDCA cycle for systematic experimentation and continuous improvement
Explore the method : Pdca improvementUse this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
Explore the method : PDFRead, create, inspect, render, and verify PDF files where visual layout matters, including fillable AcroForms. Use Poppler rendering plus Python tools such as reportlab, pdfplumber, and pypdf for generation and extraction.
Explore the method : PDFScreen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : PEAD ScreenerCalculate personal or household net worth, compound savings growth, savings rate, retirement scenarios, and financial independence or FIRE milestones from supplied assets, debts, income, expenses, and assumptions. Use for net-worth projections, how long until a savings goal, or what-if contribution and growth comparisons. Business forecasts, stock selection, tax filing, and account transactions belong elsewhere.
Explore the method : Personal finance projectionManage host project ledgers for customer delivery and internal operating work. Use to capture next steps, change task status, report project work, summarize active tasks, preserve request traceability, or draft a customer status summary. Does not manage sales CRM work or personal todos.
Explore the method : Plan workUse when planning Zoom integrations.
Explore the method : Plan Zoom IntegrationUse when choosing products.
Explore the method : Plan Zoom ProductWhen the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.
Explore the method : PopupsReview investment holdings, allocation, risk, buying power, and position health. Select the Alpaca, Interactive Brokers, or NinjaTrader account adapter explicitly; supports IB action reports and NinjaTrader stop/margin checks. A supplied export supports a limited manual review. Does not switch accounts or submit rebalancing orders.
Explore the method : Portfolio ManagerUse when sizing Public Equity Investing positions, finding equity hedges, or building an integrated position-and-hedge risk plan from a listed-equity thesis. Do not use for thesis construction, standalone event underwriting, trade execution, personal investment advice, or credit-instrument risk.
Explore the method : Portfolio Risk ManagementPlan changes to existing positions: add/trim/ladder, partial exits, breakeven or trailing stops, and PMCC/LEAPS stop protection. Select the NinjaTrader, IBKR, or crypto exit-rule method for the position type. Reports before/after risk and proposed payloads; does not place, cancel, replace, or transmit orders.
Explore the method : Position Management PlanningCalculate risk-based position sizes for long stock trades. Use when user asks about position sizing, how many shares to buy, risk per trade, Kelly criterion, ATR-based sizing, fractional-share sizing, or portfolio risk allocation. Supports stop-loss distance calculation, volatility scaling, and sector concentration checks. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Position SizerPowerbi report workflow. Diagnose and resolve Power BI performance issues in Microsoft Fabric. Use when reports load slowly, DAX queries are inefficient, semantic models need optimization, DirectQuery is underperforming, capacity is throttled, visuals render slowly, or data refresh is slow. Covers Performance Analyzer, DAX Studio, Fabric Capacity Metrics app, VOrder, query folding, incremental refresh, storage modes, Best Practice Analyzer, and Spark resource profiles for Power BI workloads. Plan, design, author, preview, publish, and manage Power BI reports across requirements, page design, local PBIR/PBIP edits, validation, screenshots, Fabric upload/download, and rebinding. Use for report lifecycle work; use semantic-model-authoring for model or DAX changes and fabriciq for data questions. Triggers: plan Power BI report, design report.
Explore the method : Powerbi report workflowDiagnose and resolve Microsoft Fabric Power BI security issues including row-level security (RLS), object-level security (OLS), column-level security (CLS), workspace permissions, sensitivity labels, service principal authentication, XMLA endpoint access, DirectLake security fallback, Entra ID app registration, and data loss prevention (DLP) policy restrictions. Use when remediate access denied errors, missing data in reports, broken visuals from OLS, RLS not filtering, sensitivity label problems, permission model conflicts, or capacity-related security issues.
Explore the method : Powerbi security reviewManages Power BI semantic models, including tables, columns, measures, relationships, field parameters, DAX, Import, DirectQuery and Direct Lake designs, sources, connections, permissions, refresh, and deployment. Load this skill before acting on a semantic model, unless it is part of publishing a PBIP project, which powerbi-report-cli owns. For natural-language data questions use fabriciq; for visuals use powerbi-report-cli.
Explore the method : Powerbi semantic model authoringUse this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.
Explore the method : PPTXUse when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.
Explore the method : Pre mortemEvaluate a local pre-trade checklist before manual order entry, blocking planless, oversized, revenge-risk, market-regime-blocked, or circuit-breaker-blocked entries while journaling the decision for trader-memory-core review. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Pre-Trade Discipline GateKalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface. Market-type-agnostic shared layer for all Kalshi skills. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Prediction Market DataReview a prediction-market operation or scaling plan using evidence gates, falsification criteria, fill-model limits, drawdown controls, and incident lessons. Produces an operations proposal; does not launch bots, submit exchange orders, sign transactions, or escalate live risk.
Explore the method : Prediction Market Operations PlanningKalshi daily and hourly range markets on crypto prices (BTC, ETH) and equity indices (S&P 500, Nasdaq-100) — bracket structure, Gaussian P(YES) modeling on price/vol, close-offset decision timing, longshot-sell edge with honest evidence bounds. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Prediction Market Price Range AnalysisVenue- and market-type-agnostic strategy, sizing, and backtesting layer for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers the durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation methodology. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Prediction Market StrategyDaily temperature high/low bracket and threshold contracts on Kalshi — contract structure, forecast→P(YES) map, settlement rules, cross-venue divergences, and weather-specific pitfalls. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Prediction Market Weather AnalysisBuilds decks with a story spine, house visual system, setting-specific density, and speaker notes. Use when asked to "create a presentation", "write a pitch deck", or "turn this doc into slides". Defaults to Marp; use an available presentation tool for editable PowerPoint. For product UI use ui-design.
Explore the method : Presentation creatorCreate strategic HTML presentations with Chart.js, design tokens, responsive layouts, copywriting formulas, and contextual slide strategies.
Explore the method : Presentation designCreate a finished, editable presentation or a complete build specification from supplied evidence. Use for PowerPoint decks, slide templates, presentation redesigns, and slide-based executive communication that require narrative design, source control, construction, and rendered-slide quality assurance.
Explore the method : Presentation productionCreate stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.
Explore the method : Presentation storylineRead, create or edit PowerPoint or Google Slides decks. Use for presentation, slide deck, PowerPoint, PPT, PPTX, or Google Slides requests.
Explore the method : PresentationsGet historical price data (OHLCV) for a stock. Use when user asks about price history, historical data, past performance, price over time, or needs data for chart analysis. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Price HistoryEvidence-first revenue, pricing, refunds, team-billing, and billing-model truth workflow for ECC. Use when the user wants a sales snapshot, pricing comparison, duplicate-charge diagnosis, or code-backed billing reality instead of generic payments advice.
Explore the method : Pricing consistency reviewAssess pricing power and design evidence-backed value metrics, commercial models, price hypotheses, and validation for a product, service, or operating-business decision.
Explore the method : Pricing strategyUse when using Probe SDK.
Explore the method : Probe SDKUse when the user wants to create, explore, adapt, refine, polish, or review visual creative such as campaigns, ads, social posts, product imagery, scenes, offers, logos, brand systems, styles, charts, decks, or related marketing and design assets.
Explore the method : ProduceAnalyze product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next.
Explore the method : Product Business AnalysisSpecifies interaction choices, action scope, reversibility, recovery, and reachable states. Use when asked to "design the flow", "should delete be undoable", "choose the control", or "review this product decision". For visual implementation use ui-design; for motion use ui-animation; for wording use copywriting.
Explore the method : Product designWhen the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.
Explore the method : Product MarketingClarify product direction before implementation: diagnose the user problem, compare bets, prioritize uncertain work, and keep proposed strategy separate from executable tasks until a person explicitly approves the handoff. Use for product ideas, roadmap choices, product decisions, discovery questions, and post-ship reconciliation; use business-requirements for an implementation-ready capability plan.
Explore the method : Product strategyCodified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.
Explore the method : Production capacity planningTake an AI-built or vibe-coded prototype toward dependable production software. Use when someone asks whether an app is production ready, has hit the limits of prototyping, needs to be hardened before launch, or needs its architecture, security, data, testing, deployment, and operations gaps found and fixed.
Explore the method : Production readiness reviewPrepare a concise, evidence-grounded brief and question set for a lawyer, accountant, tax adviser, insurance professional, compliance specialist, or other licensed adviser. Use when the user needs to understand a professional issue well enough to decide a safe next step or prepare an adviser conversation, without presenting the result as professional advice.
Explore the method : Professional adviser briefWhen the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," "building many pages for SEO," "pSEO," "generate 100 pages," "data-driven pages," or "templated landing pages." Use this whenever someone wants to create many similar pages targeting different keywords or locations. For auditing existing SEO issues, see seo-audit. For content strategy planning, see content-strategy.
Explore the method : Programmatic SeoMaintain or report a project task ledger: capture next steps, update task status, summarize work, identify blockers and review waits, and draft stakeholder status updates. Use for delivery or internal projects when task ownership, evidence, approvals, and request traceability matter; do not use it as a sales CRM or a personal task list.
Explore the method : Project status updateUse when writing or reviewing LLM prompts for production agents (system prompts, intent classifiers, response generators, structured-output schemas). Trims bloat, removes brittle hardcoded examples, eliminates anti-patterns like ticket references and ALL-CAPS NEGATIVES, and produces concise, schema-shaped, model-friendly prompts.
Explore the method : Prompt EngineeringCreate optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Explore the method : Prompt writingScope a fixed-fee project, retainer, or proposal revision from supplied sales evidence. Use when a user needs a customer-ready proposal plus an internal decision memo that makes scope, assumptions, fees, risks, and approval needs explicit.
Explore the method : Proposal scopingRank supplied prospects or run an explicitly requested public-evidence sourcing pass using explicit fit, evidence, urgency, and reachability criteria. Use when a user needs a transparent target list, prospect scorecard, or funnel-stage review without inventing intent or claiming outreach was sent.
Explore the method : Prospect prioritizationRoute Public Equity Investing workflows for listed-company and public-security research, earnings analysis, valuation, model updates, long/short pitches, catalysts, thesis tracking, ETF/index and constituent diligence, position sizing, hedging, dashboards, and investment memos. Use when the user is evaluating a public company, ticker, sector, portfolio position, event, earnings setup, or investment thesis through an investor lens, including what is priced in, what is mispriced, what proves or kills the thesis, and what to watch next. Do not use for private-company diligence, credit-first analysis, personal financial advice, generic company summaries, or generic document drafting with no public-equity investment context.
Explore the method : Public Equity InvestingWhen the user wants help with public relations, earned media, press coverage, journalist outreach, or media strategy (not pull requests). Also use when the user mentions 'PR,' 'press,' 'press release,' 'media outreach,' 'pitch a journalist,' 'get featured,' 'media list,' 'media kit,' 'press kit,' 'newsjacking,' 'news hijack,' 'HARO,' 'Qwoted,' 'Featured,' 'reporter request,' 'tech press,' 'TechCrunch,' 'thought leadership placement,' 'op-ed,' 'guest article,' 'press contacts,' 'podcast prep,' 'podcast guest,' 'prep me for this podcast,' 'how do I get press,' 'PR crisis,' 'crisis communications,' 'respond to backlash,' 'we got hacked,' 'data breach statement,' or 'holding statement.' Also covers crisis response when something goes wrong. For startup/SaaS/AI directory submissions, see directory-submissions. For product launches, see launch. For social-media engagement, see social. For cold-email outreach to prospects, see cold-email.
Explore the method : Public RelationsPublish a validated Data Analytics report or dashboard artifact through Sites. Use automatically for durable artifacts in any positively identified Work Mode when the full Sites lifecycle is callable, or in ChatGPT Desktop outside Work Mode only after an explicit Sites request or acceptance of the optional post-handoff sharing offer.
Explore the method : Publish Artifact To SitesCodified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.
Explore the method : Quality incident analysisDeploy applications on Railway platform. Use when deploying containerized apps, setting up databases, configuring private networking, or managing Railway projects. Triggers on Railway, railway.app, deploy container, Railway database.
Explore the method : Railway deploymentCall the RawUGC API to generate AI videos/images/music, manage content (personas, products, styles, characters), schedule social media posts, research TikTok content, and analyze viral videos. Use when the user wants to interact with any RawUGC API endpoint.
Explore the method : Rawugc apiReact and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Explore the method : React best practicesReact composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
Explore the method : React composition patternsReact Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
Explore the method : React Native design and performanceGuide for implementing smooth, native-feeling animations using React's View Transition API (`<ViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter/exit of components, animate list reorder, implement directional (forward/back) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.
Explore the method : React view transitionsUse when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Explore the method : Receiving Code ReviewWhen the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of mouth,' 'viral loop,' 'refer a friend,' 'partner program,' 'referral incentive,' 'how to get referrals,' 'customers referring customers,' or 'affiliate payout.' Use this whenever someone wants existing users or partners to bring in new customers. For launch-specific virality, see launch.
Explore the method : ReferralsReorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.
Explore the method : Relationship prioritizationSoftware architecture and UI/UX principles for building genuinely new solutions, not derivative work. Use when designing features, architecting software, brainstorming apps, reviewing designs, or during strategy discussions. Focuses on first-principles thinking, simplicity where it matters, and creating rather than commenting.
Explore the method : Renaissance architectureNarrow conversion skill. Invoke only when the user explicitly asks to convert an existing local or blob-hosted HTML analytics report into a Google Doc, DOCX, or shareable document.
Explore the method : Report To Google DocNarrow conversion skill. Invoke only when the user explicitly asks to convert an existing HTML analytics report into a native Google Slides deck.
Explore the method : Report To Google SlidesNarrow conversion skill. Invoke only when the user explicitly asks to convert an existing Data Analytics report, dashboard, or inline chart export into a PDF artifact.
Explore the method : Report To PDFCore skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
Explore the method : Report writingArrange an independent, read-only review of a bounded code or agent-skill change before merge or a completion claim. Use after significant behavior changes, contract-changing refactors, high-risk fixes, or materially changed skills; use review-work for fresh verification evidence, and route business decisions or ordinary document review elsewhere.
Explore the method : Requesting Code ReviewComprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems. Use when users need help with ISO 13485 QMS documentation, including (1) conducting gap analysis of existing documentation, (2) creating Quality Manuals, (3) developing required procedures and work instructions, (4) preparing Medical Device Files, (5) understanding ISO 13485 requirements, or (6) identifying missing documentation for medical device certification. Also use when users mention medical device regulations, QMS certification, FDA QMSR, EU MDR, or need help with quality system documentation.
Explore the method : Requirements gap analysisSystematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
Explore the method : Research reviewEvidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
Explore the method : Research synthesisSeparate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Residual Edge AnalyzerCreate an evidence-bounded responsibility and decision-rights map for work, decisions, handoffs, approvals, and escalations. Use to clarify accountable and responsible roles, expose conflicts and gaps, distinguish action ownership from authority, and produce a RACI-style operating view without inventing assignments.
Explore the method : Responsibility mappingCodified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.
Explore the method : Returns case analysisUse when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Explore the method : Review workWhen the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.
Explore the method : RevopsUse when using Rivet SDK.
Explore the method : Rivet SDKCodified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.
Explore the method : Root cause analysisReview a completed sales, discovery, or account conversation for evidence-backed coaching on discovery, qualification, objections, commitments, follow-up, and next-call language. Use when someone wants to improve from a transcript or meeting note. Internal only: do not alter relationship records, tasks, drafts, or customer-facing material.
Explore the method : Sales call coachingDesign an evidence-based storyline for a sales brief, brochure, deck, proposal insert, or seller email. Use when a user needs the argument, proof structure, call to action, and content plan before designing or publishing collateral.
Explore the method : Sales collateral storylineWhen the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'battle card,' 'battlecard,' 'competitive one-pager,' 'win-loss analysis,' 'why are we losing deals,' 'loss reasons,' 'how do I respond to this objection,' 'prospect just said,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps.' Use this for any document or asset that helps a sales team close deals. For public competitor comparison pages, see competitors. For running buyer interviews or churn research, see customer-research. For marketing website copy, see copywriting. For cold outreach emails, see cold-email. For the offer being sold (bonuses, guarantees, pricing structure), see offers.
Explore the method : Sales EnablementPrepare an evidence-based internal brief for a sales or discovery meeting. Use when a user needs account context, stakeholder hypotheses, discovery questions, risks, value hypotheses, and a clear next decision without inventing research or customer intent.
Explore the method : Sales meeting preparationReview supplied sales-pipeline data for stage hygiene, value separation, follow-up gaps, risk, and next actions. Use when a user needs a reliable pipeline digest, forecast caveats, opportunity review, or proposed record updates without silently changing a CRM.
Explore the method : Sales pipeline reviewScan stocks for bullish trends using technical indicators (SMA, RSI, MACD, ADX). Use when user asks to scan for bullish stocks, find trending stocks, or rank symbols by momentum. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Scanner BullishScan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, yield, trend direction, and earnings proximity. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Scanner PmccSkill that analyzes 18-month scenarios from a news headline. Runs the primary analysis with the scenario-analyst agent and obtains a second opinion with the strategy-reviewer agent. Generates a comprehensive English report covering 1st/2nd/3rd-order impacts, recommended stocks, and a critical review. Example: /scenario-analyzer "Fed raises rates by 50bp" Triggers: news analysis, scenario analysis, 18-month outlook, medium-to-long-term investment strategy. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Scenario AnalyzerRun structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like "what if...", "what would happen if...", "what are the possibilities", "explore scenarios", "scenario analysis", "possibility space", "what could go wrong", "best case / worst case", "risk analysis", "contingency planning", "strategic options", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.
Explore the method : Scenario planningUse when turning a public-equity base case, model, thesis, event, or catalyst into scenario skew, sensitivity, breakpoint, and PM action-threshold analysis. Do not use for first-pass model builds, credit-security valuation, or generic planning.
Explore the method : Scenario Sensitivity GeneratorDesign, critique, route, and implement Gantt charts and schedule visualizations. Use when the user mentions Gantt charts, project schedules, roadmaps with task spans, milestones, dependencies, predecessors, critical path, baselines, WBS, resource plans, capacity timelines, MS Project, Primavera P6, Jira Advanced Roadmaps, GitHub Projects, Smartsheet, monday.com, Asana, ClickUp, Azure DevOps iterations, or importing/exporting project-management data for a timeline chart.
Explore the method : Schedule planningWhen the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.
Explore the method : SchemaUse when using Scribe.
Explore the method : ScribeAudit, plan, and implement SEO improvements across technical SEO, on-page optimization, structured data, Core Web Vitals, and content strategy. Use when the user wants better search visibility, SEO remediation, schema markup, sitemap/robots work, or keyword mapping.
Explore the method : Search content planningThis skill should be used when analyzing sector rotation patterns and market cycle positioning. It fetches sector uptrend data from CSV (no API key required) and optionally accepts chart images for supplementary analysis. Use this skill when the user requests sector rotation analysis, cyclical vs defensive assessment, overbought/oversold identification, or market cycle phase estimation. All analysis and output are conducted in English. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Sector AnalystUse this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.
Explore the method : Security ReviewWhen the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.
Explore the method : Seo AuditUse when setting up OAuth.
Explore the method : Setup Zoom OauthUse when building Zoom webhooks.
Explore the method : Setup Zoom WebhooksUse when building Zoom WebSockets.
Explore the method : Setup Zoom WebsocketsManages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".
Explore the method : shadcn/uiCodified expertise for customs documentation, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Informed by trade compliance specialists with 15+ years experience. Includes HS classification logic, Incoterms application, FTA utilization, and penalty mitigation. Use when handling customs clearance, tariff classification, trade compliance, import/export documentation, or duty optimization.
Explore the method : Shipment document reviewReview closed trade outcomes or reconstruct one trade with MFE, MAE, and alternate-exit counterfactuals from its actual fills and bars. Use the NinjaTrader replay method for a single-trade deep dive; counterfactual replay is distinct from a historical strategy backtest.
Explore the method : Signal PostmortemWhen the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "registration friction," "signup form optimization," "free trial signup," "reduce signup dropoff," "account creation flow," "people aren't signing up," "signup abandonment," "trial conversion rate," "nobody completes registration," "too many steps to sign up," or "simplify our signup." Use this whenever the user has a signup or registration flow that isn't performing. For post-signup onboarding, see onboarding. For lead capture forms (not account creation), see cro.
Explore the method : SignupWhen the user wants to plan, map, or restructure their website's page hierarchy, navigation, URL structure, or internal linking. Also use when the user mentions "sitemap," "site map," "visual sitemap," "site structure," "page hierarchy," "information architecture," "IA," "navigation design," "URL structure," "breadcrumbs," "internal linking strategy," "website planning," "what pages do I need," "how should I organize my site," or "site navigation." Use this whenever someone is planning what pages a website should have and how they connect. NOT for XML sitemaps (that's technical SEO — see seo-audit). For SEO audits, see seo-audit. For structured data, see schema.
Explore the method : Site ArchitectureCreate, import, rewrite, or improve a reusable agent skill or SKILL.md package. Use when defining triggers and scope, auditing an upstream skill, adding resources, designing evaluations, or fixing a skill that routes or performs poorly; route one-off prompts and live integrations elsewhere.
Explore the method : Skill authoringAsk which skill or flow fits your situation. A router over the user-invoked skills in this repo.
Explore the method : Skill discoveryDesign, run, and interpret evidence-based evaluations for an agent skill or workflow. Use for skill test cases, regression checks, baseline comparisons, qualitative review, benchmark design, trigger testing, and reliability claims. Keeps authored plans, executed results, and human approval distinct.
Explore the method : Skill evaluationCapture, triage, and resolve review feedback for a collaboratively maintained agent skill. Use when feedback needs a durable ledger, when recording a fix and its evidence, when reporting open or blocked findings, or when turning evaluated feedback into a bounded revision. Does not infer approval or close findings without evidence.
Explore the method : Skill refinementWhen the user wants to plan, build, or optimize SMS, MMS, or WhatsApp marketing — including welcome flows, abandoned cart texts, post-purchase, win-back, promotional sends, or transactional/auth SMS. Also use when the user mentions "SMS marketing," "text message campaigns," "SMS sequence," "SMS automation," "abandoned cart text," "post-purchase SMS," "Klaviyo SMS," "Postscript," "Attentive," "Twilio," "A2P 10DLC," "TCPA," "SMS compliance," "short code," "toll-free SMS," "MMS campaign," "should I do SMS," "SMS vs email," "WhatsApp marketing," "WhatsApp Business API," "WhatsApp templates," or "click-to-WhatsApp." For email sequences, see emails. For SMS copy framing, see copywriting. For opt-in popups that capture phone numbers, see popups.
Explore the method : SmsCreate, repurpose, schedule, and improve social media content for LinkedIn, X, Instagram, TikTok, Facebook, Reels, Shorts, carousels, threads, and content calendars. Use when a user asks for social posts, threads, platform strategy, content calendars, repurposing, short-form video scripts, hooks, or how to grow a relevant audience.
Explore the method : Social contentCLI and agent skill for personal LoRAs, image, video, and music generation using Sogni AI's decentralized GPU network. Supports Pixal3D single- and multi-view image-to-GLB, BiRefNet background removal, Qwen3-TTS speech/voice cloning/design, MiniMax Music 3, SAM 3 object selection, promptless RTX VSR image upscaling through 16K, promptless FlashVSR video upscaling to 1080p/1440p, one-click image-folder loop reels, personas (named people with saved reference photos and voice clips), persistent memories, custom personality, style transfer, angle synthesis, MiniMax H3/H3 Balanced/LightX2V Turbo/FastH3 Turbo (including audio-to-video from an uploaded track)/Seedance/HappyHorse/LTX/WAN video, music/lyrics, hosted chat, durable workflows, replay records, and multi-step creative workflows. Ask the agent to "draw", "generate", "create an.
Explore the method : Sogni creative media productionTurn a folder of images into a polished, music-backed seamless loop using visually deduplicated source frames and direct LTX first-frame/last-frame transitions. Invoke as /sogni-creative-agent:loop-maker IMAGE_FOLDER in Claude Code or $sogni-creative-agent:loop-maker in Codex, with optional start, music, output, pacing, or aspect preferences. Use when the user asks for a one-click image-folder reel, animated photo loop, looping montage, or slideshow where every image animates briefly before morphing into the next. Do not route true 360 novel-view synthesis here.
Explore the method : Sogni image-folder loop reelsPre-execution Solana transaction streaming via Jito ShredStream, Shyft RabbitStream, and Triton Deshred. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Solana Market StreamingSolana transaction construction including instruction building, account resolution, compute budget, priority fees, and versioned transactions. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Solana Transaction PlanningComprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems. Use when users need help with ISO 13485 QMS documentation, including (1) conducting gap analysis of existing documentation, (2) creating Quality Manuals, (3) developing required procedures and work instructions, (4) preparing Medical Device Files, (5) understanding ISO 13485 requirements, or (6) identifying missing documentation for medical device certification. Also use when users mention medical device regulations, QMS certification, FDA QMSR, EU MDR, or need help with quality system documentation.
Explore the method : Sop writingUse skill when user requests to create, modify, analyze, visualize, or work with spreadsheet files (`.xlsx`, `.xls`, `.csv`, `.tsv`) or Google Sheets with formulas, formatting, charts, tables, and recalculation. Do not use for live controlling Microsoft Excel app or a live Excel session.
Explore the method : SpreadsheetsDruckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener) into a unified conviction score (0-100), pattern classification, and allocation recommendation. Use when user asks about overall market conviction, portfolio positioning, asset allocation, strategy synthesis, or Druckenmiller-style analysis. Triggers on queries like "What is my conviction level?", "How should I position?", "Run the strategy synthesizer", "Druckenmiller analysis", "総合的な市場判断", "確信度スコア", "ポートフォリオ配分", "ドラッケンミラー分析". Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stanley Druckenmiller InvestmentUse when starting Zoom work.
Explore the method : StartGuided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
Explore the method : Statistical interpretationA set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
Explore the method : Status updateGet timestamped stock quote with price, volume, change, market cap, and 52-week range for any ticker symbol. Use when user asks about current stock price, quote, or basic stock info. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stock QuoteBuild and maintain a Stockbee-style daily 20% mover study for US equities by scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns. Use when the user asks to run a daily 20% study, backfill historical 20% movers, find recurring edge patterns, or build a model book of explosive market moves. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stockbee 20% StudyAnalyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stockbee Episodic Pivot AnalyzerScreen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, exhaustion setup, selling exhaustion, hammer reversal, undercut reclaim, near-close reversal candidates, or pullback entries in high-quality funds-owned stocks. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stockbee Exhaustion Hammer ScreenerScreen US stocks for Stockbee-style short-term Momentum Burst setups using 4% breakout, dollar breakout, range expansion, volume expansion, prior range contraction, close-location, failure filters, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, momentum burst, 4% breakout, range expansion, dollar breakout, short-term swing momentum candidates, or 3-5 day burst setup review. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stockbee Momentum Burst ScreenerBuild a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Stockbee Setup Fluency TrainerBuild AI agents with the Strands Agents SDK - the open-source framework (the agent "brain") for writing agent logic, tools, and multi-agent systems in Python. Model-agnostic, AWS Bedrock by default. Covers Agent, the @tool decorator, model providers (BedrockModel), multi-agent patterns (agents-as-tools, Swarm, Graph), conversation management, and streaming. Every import verified against official Strands docs. To DEPLOY a Strands agent on AWS, use the aws-harness skill. Triggers on Strands, Strands Agents, Strands SDK, agent framework, agents as tools, Swarm, Graph multi-agent, BedrockModel.
Explore the method : Strands agent developmentExecute a reproducible trading-strategy backtest using the selected available engine: manifoldbt, vectorbt, or event-driven Backtrader. Requires real historical bars, explicit rules, costs, and an executable host. Returns measured results and assumptions; backtest-expert owns independent result scrutiny. Never substitutes estimated metrics for an actual run.
Explore the method : Strategy backtestingDetect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Strategy Pivot DesignerUse when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
Explore the method : Structured reasoningUse when executing implementation plans with independent tasks in the current session
Explore the method : Subagent Driven DevelopmentUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Explore the method : Systematic DebuggingTrains a classifier to distinguish train from test data, detecting distribution shift and identifying leaked features.
Explore the method : Tabular ML: adversarial validationDetects sentinel anomaly values in numeric columns, creates a boolean flag feature, then replaces the sentinel with NaN for proper imputation.
Explore the method : Tabular ML: anomaly flag imputationProbe an anonymized regression target by testing whether simple invertible transforms (2**y, exp(y), log(y), affine rescale) produce a distribution with recognizable structure — round numbers, integer histograms, or a familiar finance/retail range — and use the recovered semantics to motivate features and loss choices the host's bland description would never suggest
Explore the method : Tabular ML: anonymized target reverse engineeringSplit a transaction table by a binary status flag (authorized vs. declined, paid vs. refunded) into two parallel sub-tables, then build the same aggregate feature pipeline on each — the declined-transaction features are usually as predictive as the authorized ones because they encode risk and friction the authorized stream alone hides
Explore the method : Tabular ML: authorized flag table splitTrain a PyTorch autoencoder on time-series summary statistics to produce dense encoded features for downstream GBDT models
Explore the method : Tabular ML: autoencoder timeseries embeddingClass-balanced log loss that weights each class by the inverse of its sample count, equalizing the contribution of minority and majority classes.
Explore the method : Tabular ML: balanced log lossPredict where a projectile will land using kinematic equations with estimated gravity to intercept aerial passes in game AI simulations
Explore the method : Tabular ML: ball landing predictionRecover missing categorical values by matching words in a related text field against a known vocabulary built from the full dataset
Explore the method : Tabular ML: brand name recovery from titleCatBoostRegressor with MultiRMSE loss for native multi-output regression, predicting all targets in a single model without per-target loops.
Explore the method : Tabular ML: catboost multirmsePredict correlated targets sequentially, using earlier target predictions as input features for subsequent targets to exploit inter-target dependencies
Explore the method : Tabular ML: chained target predictionStreams large HDF5 files in fixed-size row chunks to compute summary statistics without loading the full dataset into memory.
Explore the method : Tabular ML: chunked hdf5 streamingRecommends items frequently purchased together with a customer's recent items using pre-computed pair dictionaries.
Explore the method : Tabular ML: co purchase item pairingRemoves redundant features by iterating pairwise Pearson correlations and dropping one member of each pair exceeding a threshold.
Explore the method : Tabular ML: collinear feature removalAugments imbalanced tabular data by independently shuffling each feature column within a class, creating synthetic samples that preserve per-column marginal distributions.
Explore the method : Tabular ML: column shuffle augmentationHard-clips predicted probabilities to 0 or 1 when they exceed high-confidence thresholds, reducing log loss on near-certain predictions.
Explore the method : Tabular ML: confidence probability clippingEncodes categorical groups by their target rate scaled by a log-confidence factor, smoothing unreliable rates from low-frequency groups toward zero.
Explore the method : Tabular ML: confidence weighted rate encodingPrecomputes item/content difficulty as historical mean accuracy, merged as a static feature for user-item prediction tasks.
Explore the method : Tabular ML: content difficulty featuresMinimize axis-aligned bounding box side length by finding the optimal rotation angle over convex hull vertices using bounded scalar optimization
Explore the method : Tabular ML: convex hull bbox rotationBuild user-level behavioral features (avg listing duration, relisting frequency, total items) by joining auxiliary activity tables that share user_id but not item_id with train/test
Explore the method : Tabular ML: cross dataset user aggregationModel cumulative distribution via softmax output layer and CRPS loss — for probabilistic regression over discrete bins
Explore the method : Tabular ML: crps cdf lossEncodes cyclical features (hour, month, day-of-week) using sine/cosine transforms to preserve circular distance.
Explore the method : Tabular ML: cyclical feature encodingConfig-driven feature factory that generates groupby aggregation features from a declarative spec list, supporting count, mean, var, nunique, cumcount, and custom lambdas.
Explore the method : Tabular ML: declarative groupby aggregationReshapes model predictions to match the known label distribution from training data using rank-based mapping.
Explore the method : Tabular ML: distribution matching postprocessPredefines minimal unsigned integer dtypes before CSV loading to cut DataFrame memory usage by 2-4x without any data loss.
Explore the method : Tabular ML: dtype preset csv loadUse sklearn FeatureUnion with closure-based preprocessors to apply different vectorizers to different DataFrame columns in a single fit_transform call
Explore the method : Tabular ML: featureunion field dispatchAdds each feature's value-count frequency as a new column, enabling tree models to split on how common or rare a value is.
Explore the method : Tabular ML: frequency encodingEncode a 2D game board into a normalized multi-channel feature tensor with log-scaled resources, signed unit counts, and directional features for RL agents
Explore the method : Tabular ML: game state grid encodingFits a Gaussian Mixture Model on the joint feature-target space and samples synthetic data pairs to augment small tabular datasets.
Explore the method : Tabular ML: gmm feature augmentationConstructs a customer similarity graph via KNN on mixed features, then trains a GraphSAGE GNN for node classification. Captures relational patterns that tree and linear models miss, adding ensemble diversity.
Explore the method : Tabular ML: gnn on knn graphUses GroupKFold to prevent data leakage when multiple rows share a common entity (e.g., same user, question, or document).
Explore the method : Tabular ML: group kfold leak preventionCustom evaluation metric that computes log of per-group MAE then averages, penalizing uniformly bad groups.
Explore the method : Tabular ML: group mean log mae metricSplits train/validation using GroupShuffleSplit so that related samples (forks, families, sessions) never span both sets.
Explore the method : Tabular ML: group shuffle splitGenerates geographically proximate candidate pairs for entity matching using KNN with haversine distance, optionally partitioned by country.
Explore the method : Tabular ML: haversine knn candidate generationThree-level polygon overlap test — AABB early exit, then point-in-polygon ray casting, then segment intersection — for fast non-convex collision detection
Explore the method : Tabular ML: hierarchical collision cascadeTwo-level group-then-pattern dispatch for game AI agents where groups filter by game state and ordered patterns within a group fire the first matching action
Explore the method : Tabular ML: hierarchical rule engineAlternating Least Squares matrix factorization on sparse user-item interaction matrices for implicit feedback recommendations.
Explore the method : Tabular ML: implicit als collaborative filteringComputes leak-free target encoding statistics (mean, std, min, max) using nested inner KFold within each outer CV fold, preventing target leakage that occurs with naive groupby-based encoding.
Explore the method : Tabular ML: inner kfold target encodingMulti-round pseudo labeling with progressively confident test predictions merged into training plus OOF-based train label correction
Explore the method : Tabular ML: iterative pseudo label refinementExtract and visualize per-subgroup feature coefficient signs from L1-regularized models as an interaction heatmap for EDA
Explore the method : Tabular ML: l1 coefficient interaction mapCompute first-order difference between last and second-to-last rows per entity in panel data to capture recent trend direction and magnitude
Explore the method : Tabular ML: last diff lag featuresIterates through rows chronologically to accumulate user statistics, fetching current state before updating to prevent future data leakage.
Explore the method : Tabular ML: leak free loop featuresUse LightGBM DART boosting (dropout on trees) with aggressive feature and bagging fractions to reduce overfitting on high-dimensional tabular data
Explore the method : Tabular ML: lgbm dart boostingAverage model predictions across CV folds in log-odds space rather than probability space for better-calibrated ensemble outputs
Explore the method : Tabular ML: log odds fold averagingApplies logit transformation to base model probabilities before fitting a logistic regression meta-learner, enabling principled linear combination in log-odds space.
Explore the method : Tabular ML: logit transform stackingRerank session candidates using log-spaced recency weights multiplied by interaction-type multipliers
Explore the method : Tabular ML: logspace recency rerankingBlend multiple submission CSVs by row-wise majority voting on discrete predictions to produce a more robust final output
Explore the method : Tabular ML: majority vote submission blendConverts molecular SMILES strings to fixed-length Morgan fingerprint bit vectors using RDKit for use as tabular ML features.
Explore the method : Tabular ML: morgan fingerprint featuresKeras multi-input model with separate embedding layers for categoricals, GRU for text sequences, and dense layers for numerics, all concatenated into a shared regression trunk
Explore the method : Tabular ML: multi input embedding nnNeural network with multiple output heads for main target plus auxiliary targets, improving representation learning via shared layers.
Explore the method : Tabular ML: multi output auxiliary targetsTrains multiple models per CV fold with different random seeds for augmentation, then averages their predictions to reduce variance from stochastic data generation.
Explore the method : Tabular ML: multi seed fold averagingFuse recommendation candidates from user history, multiple co-visitation matrices, and global popularity in a priority-ordered cascade
Explore the method : Tabular ML: multi source candidate fusionTrains on a finer-grained multiclass target (subtypes), then collapses non-baseline classes into a single positive class for binary submission.
Explore the method : Tabular ML: multiclass to binary collapseUse scipy Nelder-Mead simplex to optimize regression-to-ordinal thresholds maximizing quadratic weighted kappa on OOF predictions
Explore the method : Tabular ML: nelder mead threshold optimizationComputes seconds until the next event within a group using diff().shift(-1) on sorted timestamps, capturing user behavior velocity.
Explore the method : Tabular ML: next click time deltaCreates bi-gram and tri-gram composite categorical features by concatenating top categorical columns, then target-encodes the composites. Captures interaction effects that tree models may miss.
Explore the method : Tabular ML: ngram composite featuresScores features by comparing actual importances against a null distribution from shuffled targets, removing features that cannot beat random noise.
Explore the method : Tabular ML: null importance feature selectionGenerates out-of-fold predictions from auxiliary models and uses them as input features for the final model.
Explore the method : Tabular ML: oof meta featuresScore candidate movement directions by average distance to nearby opponents and pick the safest path for ball-carrying agents in game AI
Explore the method : Tabular ML: opponent avoidance scoringUses Optuna with TPE sampler for Bayesian hyperparameter optimization of LightGBM, searching key params like num_leaves, depth, and learning rate.
Explore the method : Tabular ML: optuna lgbm tuningWhen a regression target has a long discrete tail (e.g. ~1% of rows pinned at -33.22 in Elo), train one regressor on the *non-outlier* subset, a separate binary classifier for the outlier flag, and splice the predictions — replace the top-K most-confident outlier predictions in the regressor's output with the outlier value, where K is calibrated on validation
Explore the method : Tabular ML: outlier aware two stage blendingEncode a categorical column by replacing each category with the per-category outlier rate (mean of a binary outlier flag), out-of-fold to avoid leakage — a target-aware encoding tuned to long-tail / sentinel-target problems where a binary classifier signal is more useful than the raw regression mean
Explore the method : Tabular ML: outlier rate label encodingGenerates all C(n,2) pairwise feature combinations, target-encodes each pair with cuML TargetEncoder, then applies logit polynomial expansion (z, z^2, z^3) for stacking with cuML LogisticRegression.
Explore the method : Tabular ML: pairwise te logit stackingUses negative row-wise Pearson correlation as a differentiable loss function for multi-output regression, directly optimizing the competition metric.
Explore the method : Tabular ML: pearson correlation lossPost-processing correction for multi-output regression — scale each output by its train-derived mean ratio to fix systematic per-feature bias
Explore the method : Tabular ML: per feature bias correctionBinarizes each CV fold's predictions using its own optimized threshold, then majority-votes across folds instead of averaging raw probabilities.
Explore the method : Tabular ML: per fold threshold voting ensembleApply VarianceThreshold within each data partition on combined train+test to select informative features per subgroup
Explore the method : Tabular ML: per partition variance filteringTrains independent models per target by masking NaN labels, enabling multi-output regression on datasets where each target has different coverage.
Explore the method : Tabular ML: per target nan mask trainingTrains separate models for each discrete category (e.g., molecule type, product class) to capture type-specific patterns.
Explore the method : Tabular ML: per type model trainingParse structured text fields like '1 RB, 2 TE, 2 WR' into separate numeric columns per category
Explore the method : Tabular ML: personnel count parsingDivide a game into repeating phases (attack, mine, spawn) with turn-modular gating so the agent cycles between aggressive and economic behavior
Explore the method : Tabular ML: phase based strategy cyclingMirror spatial coordinates and angles so all plays face the same direction — removes left/right asymmetry from sports and spatial data
Explore the method : Tabular ML: play direction normalizationGenerates polynomial powers and interaction terms from selected numeric features to capture nonlinear relationships with the target.
Explore the method : Tabular ML: polynomial interaction featuresFills unfilled recommendation slots with globally popular recent items to handle cold-start users and short lists.
Explore the method : Tabular ML: popularity fallback recommendationWrap a Kaggle competitive game environment as an OpenAI Gym env with continuous action space for training PPO agents via stable-baselines3
Explore the method : Tabular ML: ppo gym wrapper kaggle envPost-hoc rescales ensemble probabilities by the inverse of each class's estimated total mass across the test set, correcting for class imbalance in predictions.
Explore the method : Tabular ML: predicted class mass reweightingRebalances training data by oversampling the majority class to match a known test-set class prior, reducing prediction miscalibration.
Explore the method : Tabular ML: prior rebalancing oversamplingAugments training data with high-confidence test predictions as pseudo labels, retrains the model, and keeps the result only if OOF AUC improves. A semi-supervised technique for tabular competitions.
Explore the method : Tabular ML: pseudo labelingEnsembles multiple model predictions by converting to ranks, averaging, and normalizing back to [0,1].
Explore the method : Tabular ML: rank averaging ensembleBlends predictions from multiple models by converting to ranks, weighting, and calibrating back to probabilities via rank-group means from a reference model. Ensures monotonic calibrated output.
Explore the method : Tabular ML: rank calibrated blendingComputes all numeric RDKit molecular descriptors from SMILES strings, filtering out NaN, constant, and infinite values to produce a clean feature matrix.
Explore the method : Tabular ML: rdkit molecular descriptorsGenerates recommendation candidates by ranking a customer's purchase history by frequency and recency within a recent window.
Explore the method : Tabular ML: recency weighted candidate generationEncode closed rectangular patrol routes as compact direction-distance strings for fleet pathfinding on toroidal game grids
Explore the method : Tabular ML: rectangular flight plan encodingUses RFE with a tree estimator to iteratively remove least important features, selecting an optimal compact feature set.
Explore the method : Tabular ML: recursive feature eliminationConvert a scalar regression prediction into a smoothed CDF over discrete bins using a linear ramp instead of a hard step
Explore the method : Tabular ML: regression to cdf smoothingConverts regression predictions to ordinal classes by optimizing bin thresholds to maximize Quadratic Weighted Kappa.
Explore the method : Tabular ML: regression to ordinal thresholdingUse QuadraticDiscriminantAnalysis with regularization for binary classification on data with Gaussian cluster structure
Explore the method : Tabular ML: regularized qda classifierComputes differences and ratios between group-level aggregates and raw values to capture how each sample deviates from its group.
Explore the method : Tabular ML: relative deviation featuresTwo-stage stacking where Ridge regression on OHE+scaled features produces OOF predictions fed as an extra feature to XGBoost, letting the tree model correct non-linear residuals on top of captured linear patterns.
Explore the method : Tabular ML: ridge xgb stackingCustom Keras RMSLE metric using K.log with K.clip to safely evaluate price and count regression during training
Explore the method : Tabular ML: rmsle keras metricEngineers row-wise statistical features (sum, mean, std, skew, kurtosis, median, min, max) across all numeric columns per sample.
Explore the method : Tabular ML: row aggregate featuresNormalizes each sample's multi-output target vector to zero mean and unit variance, removing per-sample scale differences before training.
Explore the method : Tabular ML: row wise target normalizationMap calendar dates to categorical season phases (offseason, preseason, regular, postseason) using np.select with boundary date conditions
Explore the method : Tabular ML: season phase labelingSimulated annealing with diverse move operators (translate, rotate, swap, Levy flight, squeeze) and adaptive reheating on stagnation for combinatorial optimization
Explore the method : Tabular ML: simulated annealing multi operatorAugments molecular datasets by generating multiple randomized SMILES strings for the same molecule, exploiting SMILES non-uniqueness to multiply training samples.
Explore the method : Tabular ML: smiles randomization augmentationTrain LightGBM directly on a scipy.sparse.hstack of TF-IDF text vectors and dense tabular columns, passing feature_name and categorical_feature so native categorical handling survives the sparse block
Explore the method : Tabular ML: sparse dense hstack lgbmCompute min/max/mean/std of Euclidean distances from all entities to a key point, then aggregate per group for spatial feature engineering
Explore the method : Tabular ML: spatial distance aggregationThree-stage packing refinement — uniform squeeze toward centroid, greedy compaction per object, then multi-directional local search — to tighten solutions after metaheuristic optimization
Explore the method : Tabular ML: squeeze compact local searchOnline inference pattern that processes test batches sequentially, updating feature dictionaries incrementally for time-series prediction APIs.
Explore the method : Tabular ML: streaming prediction apiUse Shapely STRtree spatial index for O(n log n) polygon overlap detection instead of brute-force O(n^2) pairwise checks
Explore the method : Tabular ML: strtree spatial index collisionBuild hierarchical features for transaction panels by aggregating twice — first groupby (entity, sub-key) to get a per-(entity, sub-key) summary, then groupby (entity) on those summaries to compute mean/min/max/std across the sub-keys, capturing the *distribution* of per-customer behavior rather than a single flat mean
Explore the method : Tabular ML: successive groupby aggregatesCompresses high-dimensional targets with TruncatedSVD, trains on the reduced space, then reconstructs full predictions via the components matrix.
Explore the method : Tabular ML: svd target reconstructionDetects synthetic/fake test samples by checking whether each row has at least one unique value across all features — real samples do, synthetic ones don't.
Explore the method : Tabular ML: synthetic sample detectionWrap PyTorch TabNet in a scikit-learn BaseEstimator with built-in imputation and early stopping for use in VotingRegressor ensembles
Explore the method : Tabular ML: tabnet sklearn wrapperEnsembles TabPFN (a prior-fitted Bayesian transformer for small tabular data) with XGBoost, averaging probabilities for stronger predictions on datasets under 1000 rows.
Explore the method : Tabular ML: tabpfn small dataset ensembleReshapes tabular features into 2D pseudo-images via random feature permutation, enabling CNN-based feature interaction learning.
Explore the method : Tabular ML: tabular to image cnnBuilds user-level features by accumulating statistics across sequential event sessions before each assessment point.
Explore the method : Tabular ML: temporal session aggregationCompress TF-IDF sparse text vectors into a handful of dense TruncatedSVD components so GBDTs can consume free-text fields as plain tabular columns
Explore the method : Tabular ML: tfidf svd dense text featuresConvert per-group categorical event counts into TF-IDF-style features using log(1+tf/total) * log(N/df)
Explore the method : Tabular ML: tfidf weighted category countsParallel-load per-subject parquet time-series files with ThreadPoolExecutor and flatten describe() statistics into tabular feature vectors
Explore the method : Tabular ML: threaded parquet describe featuresShape RL rewards with time-decaying asset weights and time-increasing resource weights so the agent transitions from expansion to accumulation as the game progresses
Explore the method : Tabular ML: time varying reward shapingCompute shortest Manhattan distance on a toroidal (wrapping) grid by comparing normal vs wrap-around routes in each axis
Explore the method : Tabular ML: toroidal manhattan distanceFit unsupervised transforms (scaler, PCA, variance filter) on combined train+test data for more stable statistics, especially on small datasets
Explore the method : Tabular ML: transductive train test transformPost-processes entity match predictions to enforce symmetry (A→B implies B→A) and transitivity (A→B, B→C implies A→C) via graph closure.
Explore the method : Tabular ML: transitive match closureAggregates deeply nested relational tables through two groupby levels (child → intermediate → parent) to build features from multi-hop relationships.
Explore the method : Tabular ML: two level hierarchical aggregationBuild item co-visitation matrix from session pairs within a time window, weighting by interaction type (click/cart/order) via GPU self-join
Explore the method : Tabular ML: type weighted covisitation matrixAggregate panel/sequential data with type-appropriate statistics — numeric (mean/std/min/max/last) and categorical (count/last/nunique) — then concat into flat features
Explore the method : Tabular ML: typed panel aggregationEnsemble ranked recommendation lists by outer-joining exploded candidates and re-ranking by weighted vote sum
Explore the method : Tabular ML: vote ensemble outer joinMap free-text categorical descriptions to ordinal numeric scores via keyword matching — captures ordered severity in a single dense feature
Explore the method : Tabular ML: weather ordinal encodingCustom ranking metric combining normalized weighted Gini coefficient with top-K% capture rate for imbalanced classification with class-weighted evaluation
Explore the method : Tabular ML: weighted gini top recall metricEnsembles multiple ranked recommendation lists by scoring items as model_weight / position_rank, then re-ranking.
Explore the method : Tabular ML: weighted position decay ensembleEvaluate recommendation quality with recall@K per action type, combined via business-importance weights
Explore the method : Tabular ML: weighted recall multi objective metricUse XGBoost DeviceQuantileDMatrix with a custom batch iterator to train on large datasets without exhausting GPU memory
Explore the method : Tabular ML: xgb gpu batch iteratorSplit a multi-year table into per-year partitions, run the same groupby aggregation on each, then concat and gc — a pure-pandas map-reduce that survives 100M+ rows on a 16GB kernel
Explore the method : Tabular ML: yearly partitioned groupbycreates draft task file in .specs/tasks/draft/ with original user intent
Explore the method : Task briefReal-time tax liability analysis for active crypto traders with proportional cost basis, gain classification, and tax-aware trading signals. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Tax Liability TrackingTax-loss harvesting opportunity identification, scoring, and planning with wash sale compliance and annual carryforward tracking. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Tax Loss HarvestingAnalyze trading charts, calculate RSI/MACD/moving averages or crypto-native technical indicators, or render trading charts from adequate supplied observations. Choose the chart-reading, indicator-library, or rendering method for the requested output. Pure cross-asset correlation and hedge sizing belong to cross-asset-correlation; indicator code creation belongs to trading-indicator-development.
Explore the method : Technical AnalystUse when implementing any feature or bugfix, before writing implementation code
Explore the method : Test Driven DevelopmentDetect and analyze trending market themes across sectors. Use when user asks about current market themes, trending sectors, sector rotation, thematic investing, what themes are hot or cold, or wants to identify bullish and bearish market narratives with lifecycle analysis. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Theme DetectorToolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.
Explore the method : Theme FactoryUse when building or updating Public Equity Investing thesis trackers. Do not use for generic news summaries, trade pitches, or first-pass memos.
Explore the method : Thesis TrackerUse when building public-equity three-statement operating model workbooks. Default to the banker formula workbook path for new model builds; use deterministic exports only for controlled support calculations or explicit lightweight runs. Do not use for standalone workbook audits.
Explore the method : Three Statement Model BuilderOverride model predictions with last known value for low-activity or low-density entities where learned trends are unreliable
Explore the method : Time series: activity threshold lastval fallbackMultiply per-timestep regression loss by a 0/1 availability mask so missing future steps contribute zero gradient
Explore the method : Time series: availability masked regression lossGenerate prediction intervals by repeatedly sampling from model residuals, adding to point forecasts, and taking quantiles across synthetic futures
Explore the method : Time series: bootstrapped residual prediction intervalsDetect P-bursts (fast-typing runs) and R-bursts (consecutive revisions) via polars run-length encoding over boolean event conditions
Explore the method : Time series: burst rle detectionCustom multiclass log-loss that weights per-class contributions by class frequency and domain importance, usable as both training loss and eval metric
Explore the method : Time series: class weighted multiclass loglossProcess multiple sensor modalities through separate CNN branches then fuse via a transformer with CLS token for classification
Explore the method : Time series: cnn transformer multimodal fusionSubtract paired reference frames from signal frames to cancel readout noise and common-mode bias
Explore the method : Time series: correlated double samplingConvert a density metric back to integer counts using known population, round to nearest integer, then recompute density to exploit the discrete nature of the target
Explore the method : Time series: density to count roundtripMulti-step detector calibration pipeline — ADC inversion, hot/dead pixel masking, nonlinearity correction, dark subtraction, flat-field normalization
Explore the method : Time series: detector calibration pipelineCombines dilated 1D convolutions for multi-scale receptive fields with residual bidirectional GRU layers for sequence classification.
Explore the method : Time series: dilated conv residual gruAlign low-Hz sensor data to high-fps video by anchoring a named event (e.g. ball_snap) to a known frame index and converting time offsets via fps
Explore the method : Time series: event anchored frame syncDetects discrete events (state transitions) from continuous predictions using local maxima with minimum-interval constraints.
Explore the method : Time series: event peak detectionWalk-forward stacking ensemble that trains base models on expanding windows and a meta-learner on their out-of-fold predictions across time
Explore the method : Time series: expanding window stackingEngineer SNR-derived features from irregular time series — flux ratio squared, error-weighted mean flux, and normalized amplitude/range features
Explore the method : Time series: flux snr weighted featuresEvaluate probabilistic forecasts using normalized Gaussian log-likelihood relative to naive and oracle baselines, scoring both mean accuracy and uncertainty calibration
Explore the method : Time series: gaussian log likelihood metricDetect event start/end boundaries in time series by finding extrema of the first derivative (steepest gradient points)
Explore the method : Time series: gradient event boundary detectionDetect event ingress/egress boundaries by finding steepest gradient on each side of the signal minimum in a smoothed time series
Explore the method : Time series: gradient transit phase detectionAssign different uncertainty spread coefficients per aggregation level in hierarchical forecasts, reflecting that higher aggregation yields narrower intervals
Explore the method : Time series: hierarchy level confidence coefficientsRemove gravity component from raw accelerometer data using quaternion rotation to yield linear acceleration
Explore the method : Time series: imu gravity removalWeight multi-channel signals by inverse per-channel variance with percentile clipping, emphasizing low-noise channels in aggregation
Explore the method : Time series: inverse variance channel weightingNegative log-likelihood loss over K isotropic-Gaussian trajectory modes with per-mode confidences and logsumexp stability
Explore the method : Time series: k mode gaussian nll lossPredict day-by-day via Kaggle's iter_test API while maintaining a rolling history buffer for computing lag features online
Explore the method : Time series: kaggle api streaming inferenceSingle neural network outputting all quantiles simultaneously via pinball loss over a quantile vector for joint probabilistic forecasting
Explore the method : Time series: keras multi quantile lossBucket inter-keystroke latencies into pause-duration ranges (0.5-1s, 1-1.5s, 1.5-2s, 2-3s, >3s) and count per session as hesitation features
Explore the method : Time series: keystroke pause bucket featuresInitialize a depthwise Conv1d with FIR filter coefficients as a trainable high-pass/low-pass filter for sensor signal preprocessing
Explore the method : Time series: learnable fir filterBuild a single per-row "day off" boolean from a holidays table with National/Regional/Local locale hierarchy and Work Day overrides that flip make-up working weekends back to working days
Explore the method : Time series: locale scoped holiday flagEstimate prediction uncertainty via Monte Carlo Dropout — run inference N times with dropout active and compute mean/std
Explore the method : Time series: mc dropout uncertaintyDecompose multi-output prediction into a global mean (1D model) plus per-channel residuals (2D model) with quadrature uncertainty
Explore the method : Time series: mean residual decompositionApply MixUp augmentation to padded time-series batches with Beta-distributed lambda and soft label mixing
Explore the method : Time series: mixup sequence augmentationGenerate shift/diff features at multiple lag sizes (1,2,3,5,10,20,50,100) over cursor/time/state series, then aggregate statistics per session
Explore the method : Time series: multi gap lag diff featuresGenerate lag features for multiple targets over N days by shifting evaluation dates and self-joining per entity, creating a wide feature matrix of past target values
Explore the method : Time series: multi lag target featuresComputes rolling mean/max/std at multiple window sizes plus total variation (abs first differences) for multi-resolution temporal context.
Explore the method : Time series: multi scale rolling featuresCompute log-ratio features between adjacent frequency bands as color indices to characterize spectral shape from multi-band time series
Explore the method : Time series: multiband color index featuresEstimate periodicity from irregularly sampled multi-band time series using the multiband Lomb-Scargle periodogram, then phase-fold observations
Explore the method : Time series: multiband lomb scargle periodApply tsfresh per-passband feature extraction with FFT coefficients to capture multi-band periodicity from irregular time series
Explore the method : Time series: multiband tsfresh fftSingle linear head that jointly predicts K candidate trajectories and K softmax confidences, sliced and reshaped for multimodal regression
Explore the method : Time series: multimodal trajectory headFill gaps in a daily exogenous series (oil prices, sensor feeds) by merging against a full calendar to expose NaNs, then replacing each NaN with the midpoint of its nearest valid left and right neighbors, walking outward past consecutive NaN runs
Explore the method : Time series: neighbor average nan interpolationUse scipy periodogram to identify dominant seasonal frequencies in a time series before selecting Fourier feature orders or ARIMA seasonal parameters
Explore the method : Time series: periodogram seasonality detectionTrain a single Poisson LightGBM count forecaster, then ensemble its predictions with multiple multiplicative scaling factors (alpha ≈ 1.02-1.03) to undo the systematic downward bias of Poisson regression on intermittent retail data
Explore the method : Time series: poisson lgbm bias corrected ensembleApplies rolling mean or Butterworth low-pass filter to model predictions for temporal consistency and noise reduction.
Explore the method : Time series: prediction smoothing lpfConvert point forecasts to prediction intervals by scaling with logit-transformed quantile ratios passed through a Normal CDF
Explore the method : Time series: quantile ratio scalingDerive angular velocity from consecutive quaternion frames via relative rotation and rotvec conversion
Explore the method : Time series: quaternion angular velocityTransform forecasting target to next/current ratio minus one so that optimizing MAE or squared error implicitly minimizes SMAPE
Explore the method : Time series: ratio target for smapeForecast a multi-step horizon by predicting one day ahead, writing the prediction back into the panel as the new "actual", recomputing all lag and rolling features that depend on it, then predicting the next day — turns a one-step LightGBM regressor into a 28-day forecaster without changing the model
Explore the method : Time series: recursive multistep forecastingWalk backward through a time series and multiplicatively rescale segments when jumps exceed a fraction of the running mean to correct data collection anomalies
Explore the method : Time series: retroactive outlier rescalingWalk-forward validation for ARIMA by refitting on history at each step, forecasting one step ahead, then appending the true observation — produces an honest one-step error distribution that mirrors nightly-retrained production forecasters
Explore the method : Time series: rolling refit arima forecastEncode missing sensor data with a per-modality sentinel value that survives standardization and remains detectable after scaling
Explore the method : Time series: scaled nan sentinelScaled Pinball Loss (SPL) metric for evaluating quantile forecasts, normalized by mean absolute successive differences of training data
Explore the method : Time series: scaled pinball loss1D ResNet block with Squeeze-and-Excitation channel attention for temporal sensor feature extraction
Explore the method : Time series: se residual 1d cnnRandomly zero out entire sensor modalities during training with a learned gate to handle missing modalities at inference
Explore the method : Time series: sensor modality dropoutEDA visualization of multi-modal sensor data with axvspan shading for labeled behavioral phases and contiguous span detection
Explore the method : Time series: sensor phase visualizationDetect and mask outlier data points using iterative sigma-clipping on reference frames or calibration data
Explore the method : Time series: sigma clip outlier maskingBuild per-state SNAP / event-flag interaction features by multiplying the binary flag with the sales and revenue columns segmented by state, capturing the demand uplift on government-benefit days that affects only specific geographies and product categories
Explore the method : Time series: snap event interaction featuresProcesses long sequences in fixed-size chunks while carrying RNN hidden state across chunks for memory-efficient inference.
Explore the method : Time series: stateful chunk inferenceRe-cluster retail stores by scale-normalized weekday/dayoff mean+std profiles using Ward agglomerative clustering, replacing vendor-supplied "type/cluster" labels that correlate with store size instead of demand shape
Explore the method : Time series: store profile hierarchical clusteringReduce temporal resolution by averaging consecutive frame blocks to improve SNR and compress high-cadence data
Explore the method : Time series: temporal frame binningAggregate high-dimensional spatial sensor grids into hierarchical region statistics at multiple granularities
Explore the method : Time series: tof spatial region poolingEstimate event depth by optimizing a scalar scaling factor on the in-event segment that minimizes polynomial baseline residual across the full signal
Explore the method : Time series: transit depth polynomial optimizationUse LightGBM's tweedie objective with variance_power between 1.05 and 1.2 for zero-inflated count forecasting (retail SKUs, intermittent demand, click events) — handles the "many zeros plus a heavy right tail" distribution that breaks both regression (RMSE) and classification (BCE) objectives
Explore the method : Time series: tweedie objective zero inflatedEstimate out-of-distribution class probability as the product of (1 - p_i) across all known classes, scaled by a calibrated prior
Explore the method : Time series: unknown class residual probabilityDenoise an erratic 1D series with discrete wavelet decomposition + universal soft thresholding (sigma estimated from MAD of the detail coefficients) to extract the underlying trend/seasonality without lagging the signal — a far better trend extractor than rolling means for spiky retail or sensor data
Explore the method : Time series: wavelet denoisingToken supply dynamics, vesting analysis, inflation modeling, and valuation frameworks for crypto tokens. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Token EconomicsDouble-entry bookkeeping for trading operations with ledger management, P&L statements, balance sheets, and cash flow reporting. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Trade AccountingGenerate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets. Use when you have a structured input bundle and want ranked hypothesis cards with experiment designs, kill criteria, and optional strategy.yaml export compatible with edge-finder-candidate/v1. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Trade Hypothesis IdeatorReview trading performance and behavioral patterns, including revenge risk, loss streaks, size drift, or a structured NinjaTrader session debrief. Distinguish an advisory behavioral check, retrospective statistics, and coaching hypotheses; use pre-trade-discipline-gate for a blocking checklist.
Explore the method : Trade Performance CoachRecord and review trade theses and journal evidence, or summarize a closed session with transaction costs, streaks, and hold times. Select the stateful TraderMonty journal or descriptive NinjaTrader trade-journal method. Keep proposed entries separate from confirmed writes.
Explore the method : Trader Memory CoreFeature construction from market data for ML trading models including price, volume, on-chain, and microstructure features. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Trading Feature EngineeringCreate, debug, explain, refactor, or port trading indicators for TradingView Pine Script, NinjaTrader NinjaScript, and Tradovate JavaScript. Use for indicator source code, platform lifecycle, plots, sessions, calculations, and cross-platform parity. Route market interpretation to technical-analyst and order execution elsewhere.
Explore the method : Trading indicator developmentML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold optimization. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Trading Signal ClassificationRecommend the right trading workflow, skillset, API profile, and setup path from a natural-language goal. Use this as the on-ramp when a user expresses a trading or investing goal and needs to know which skill/workflow to use, where to start, or whether something works without paid API keys — e.g. "where do I start", "which skill should I use", "I want to swing trade only when the market is favorable", "what works without API keys", "どれを使えばいい", "API キー無しで 使えるものは". Routes and explains only; it never executes trades or auto-runs other skills, and it is honest when no workflow has shipped yet. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Trading Skills NavigatorResearch across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
Explore the method : Trip briefAudits font loading, type scales, measure, spacing, OpenType, and rendered punctuation with 78 scoped rules. Use when asked to "audit typography", "fix the fonts", or "review my type system". For a new visual direction use ui-design Direction; for general UI defects use ui-design Audit.
Explore the method : Typography auditUGC video format templates for general B2C products. Contains 15 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Use when the user wants to create B2C UGC content — product demos, honest reviews, lifestyle integrations, or comparison content for any consumer product.
Explore the method : Ugc b2cScore and grade short-form UGC video ideas for quality, complexity, and nativeness. Use when the user wants to evaluate, audit, or improve UGC video scripts, hooks, overlays, and shot lists for platforms like TikTok and Reels.
Explore the method : Ugc content graderUGC video format templates for DTC (direct-to-consumer) brands. Contains 15 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Use when the user wants to create DTC UGC content — unboxings, product rituals, honest reviews, or lifestyle integrations.
Explore the method : Ugc dtcUGC video format templates for fashion brands. Contains 20 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Use when the user wants to create fashion UGC content — OOTDs, try-on hauls, styling videos, or outfit showcases.
Explore the method : Ugc fashionUGC lifestyle b-roll video templates for brands. Contains 20 TikTok-native, lofi b-roll formats with shot-by-shot structure and AI video generation prompts. Use when the user wants to create lifestyle b-roll content — aesthetic scene-setting, product-in-context shots, mood pieces, or ambient footage for ads and social.
Explore the method : Ugc lifestyle brollUGC video format templates for mobile app brands. Contains 15 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Focuses on the person and their physical relationship with their phone — reactions, rituals, and real moments — never screen content or app UI. Use when the user wants to create mobile app UGC content.
Explore the method : Ugc mobileUGC video format templates for SaaS products. Contains 15 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Focused on people talking about, reacting to, and advocating for SaaS tools — never showing screens, dashboards, or UI. Use when the user wants to create SaaS UGC content — passionate recommendations, founder stories, or personal reactions to tools they love.
Explore the method : Ugc saasUGC video format templates for skincare brands. Contains 20 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Use when the user wants to create skincare UGC content — routines, texture shots, ingredient education, or product showcases.
Explore the method : Ugc skincareUGC video format templates for supplement brands. Contains 15 TikTok-native, lofi video formats with shot-by-shot structure and AI video generation prompts. Use when the user wants to create supplement UGC content — morning stacks, ingredient education, taste tests, or wellness routines.
Explore the method : Ugc supplementsUGC video format templates for Yapper-style ads, also known as "ugly ads." Contains 15 TikTok-native, lofi video formats focused on a raw, imperfect aesthetic. These formats prioritize authenticity and a genuine, unscripted feel by providing light talking points instead of strict dialogue. Use when the user wants to create UGC that feels like a real person sharing their unfiltered thoughts.
Explore the method : Ugc yapperBuilds, reviews, and measures UI motion, including springs, gestures, scroll effects, and curve fitting from recordings. Use when asked to "add animation", "match this easing", "reverse engineer this motion", or find animation opportunities. For action semantics use product-design; for visual layout use ui-design.
Explore the method : UI animationDesigns and builds React/Next/Tailwind UI and audits visual and interaction defects. Use when asked to "build a landing page", "extract our design system", "add dark mode", "make this responsive", "remove UI slop", or "audit this component". For product decisions use product-design; for browser measurements use ui-verification; for motion use ui-animation.
Explore the method : UI designCreate beautiful, accessible user interfaces with shadcn/ui components (built on Radix UI + Tailwind), Tailwind CSS utility-first styling, and canvas-based visual designs. Use when building user interfaces, implementing design systems, creating responsive layouts, adding accessible components (dialogs, dropdowns, forms, tables), customizing themes and colors, implementing dark mode, generating visual designs and posters, or establishing consistent styling patterns across applications.
Explore the method : UI stylingUse when using Zoom UI Toolkit.
Explore the method : UI ToolkitProfessional typography rules for UI design, web applications, software interfaces, and all screen-based text. Enforces timeless typographic correctness that LLMs consistently get wrong: proper quote marks, dashes, spacing, hierarchy, and layout. ENFORCEMENT MODE: When generating ANY HTML, CSS, React, JSX, or UI code containing visible text, auto-apply every rule in this skill silently — do not ask, do not explain, just produce correct typography. AUDIT MODE: When reviewing or improving existing interfaces or legacy code, flag violations and provide fixes. Trigger on: any HTML/CSS/React artifact creation, "build a landing page", "create a component", "design a UI", "fix the typography", "make this look professional", "review this layout", web design, presentation design, dashboard creation, document generation, or any task producing visible text for humans. Even if the user doesn't mention typography, apply these rules whenever generating UI output.
Explore the method : UI typographyRuns scoped browser probes for focus, hit targets, overflow, themes, request failures, and performance attribution, with evidence linked to UI rule IDs. Use when asked to "verify this in the browser", "reproduce this finding", or "check the fix". For source audits and severity use ui-design; field metrics require RUM or CrUX.
Explore the method : UI verificationUI/UX design intelligence for web, mobile, and desktop. This skill should be used when designing, building, reviewing, or fixing interfaces, including pages, components, design systems, accessibility, interaction, responsive layout, typography, color, charts, and stack-specific UI implementation. Searchable local data: 79 searchable styles (50 active), 192 product palettes and reasoning profiles, 74 font pairings, 119 UX guidelines, 105 icons, 17 GSAP presets, 25 chart types, and 22 stacks.
Explore the method : UI/UX Pro MaxDesign statistically honest and uncertainty-aware visualizations. Use when the user needs help showing distributions, intervals, confidence, missingness, sampling effects, or analytical rigor in charts and dashboards.
Explore the method : Uncertainty communicationUpdate and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.
Explore the method : Update documentationAnalyzes market breadth using Monty's Uptrend Ratio Dashboard data to diagnose the current market environment. Generates a 0-100 composite score from 5 components (breadth, sector participation, rotation, momentum, historical context). Use when asking about market breadth, uptrend ratios, or whether the market environment supports equity exposure. No API key required. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Uptrend AnalyzerEvaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : US Market Bubble DetectorResearch a US-listed stock, inspect financial fundamentals and Piotroski inputs, or build a stock research report with technical, valuation, and risk context. Use the appropriate supplied-evidence or staskh method. Formal investment committee memos belong to investment-memo.
Explore the method : US Stock AnalysisAutonomously screen NYSE, Nasdaq, and NYSE American operating-company stocks for undervalued-growth/GARP opportunities using forward same-basis valuation, driver-derived EPS/FCF forecasts, primary-source financial verification, SBC and dilution controls, sector and cycle normalization, auditable candidate-pool coverage, and fail-closed final reporting. Use when asked to find, screen, rank, or refresh US undervalued-growth stocks, including minimal requests with no ticker list or parameters. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : US Undervalued Growth ScreenerAssess a technology or operational use case, including capability, solution landscape, evidence of value, user implications, design options, and data readiness. Use for AI or technology research, use-case analysis, vendor landscape, feasibility assessment, advisory research, a point of view, or an executive briefing. Produces a structured cited analysis; it does not select, purchase, deploy, or configure a solution.
Explore the method : Use case assessmentInitialize, inspect, save, update, forget, export, or explicitly reset the Public Equity Investing plugin's local user context, source setup, or optional automation setup. Use only when the user explicitly asks to manage Public Equity Investing saved preferences, source pointers, context storage, or recurring automation.
Explore the method : User ContextUse when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback
Explore the method : Using Git WorktreesUse when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions
Explore the method : Using SuperpowersValidate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions.
Explore the method : Validate DataAnalyze the economic and practical value of a proposed change, offer, or investment using supplied evidence. Use when a user needs a value hypothesis, ROI caveats, cost-avoidance analysis, benefit model, or pricing input without double-counting benefits or inventing a fee.
Explore the method : Value analysisScreen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Value Dividend ScreenerAuto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target
Explore the method : Value stream analysisScreen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path. Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points; in historical single-ticker mode walks a multi-year history and emits every VCP that formed with forward-outcome stats (breakout / stop-hit / timeout). Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, Stage 2 momentum stock scanning, or historical VCP pattern study on a specific ticker (e.g. FIX, TSLA). Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : VCP ScreenerEvaluate a shortlisted set of vendors against decision criteria and recommend a next step. Use for vendor comparison, score these options, final vendor recommendation, supplier selection, or deep dive on a shortlist. Produces a cited recommendation with uncertainty and follow-up questions; it does not select, purchase, or contract with a vendor.
Explore the method : Vendor evaluationSynthesize reusable vendor and industry evidence from source-backed research observations. Use for vendor intelligence summaries, vendor profiles, pricing observations, unsummarized evidence reports, or migration of reusable vendor findings. Preserves the evidence trail and does not invent, publish, or overwrite unsupported claims.
Explore the method : Vendor evidence synthesisIdentify a vendor landscape and screen candidates against confirmed acceptance criteria before deep evaluation. Use for vendor shortlist, which vendors to consider, help pick a CRM or platform, supplier landscape, or pass/fail vendor screen. Produces a cited shortlist with rejection themes and research gaps; it does not select, buy, or contract with a vendor.
Explore the method : Vendor screeningPlan and review a safe transition between service providers, platforms, or operating vendors. Use when a user needs phase gates, access and continuity planning, contract or migration dependencies, cutover readiness, rollback ownership, or stabilization follow-up.
Explore the method : Vendor transition planningDeploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. "deploy to vercel", "set up vercel", "add environment variables to vercel".
Explore the method : Vercel CLI with tokensUse for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.
Explore the method : Vercel optimizationUse when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Explore the method : Verification Before CompletionWhen the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'record a product demo,' 'feature demo video,' 'in-app demo,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.
Explore the method : VideoVideo production and caption editing. Award-winning director's brain for making films/videos with AI. Runs a gated, interrogation-first process: lock the IDEA and the SCRIPT (the story) in words before generating a single pixel, then plan shots/camera/light/sound, then generate and edit. Use whenever creating a video, film, teaser, trailer, promo, ad, short, reel, documentary, or narrative piece AND the storytelling must actually work — or any time the user says the story/script keeps failing. Hebrew triggers: סרטון, טיזר, פרומו, פרסומת, ריל, סרט, תסריט, סטוריבורד. Handles development, story, and shot-planning; composes with cinematic-ai-video and yuv-fomo-teaser (style/manipulation), hyperframes (render), nano-banana-2/ElevenLabs (assets). Backed by a 28-chapter Director's Bible in references/. Edit any video into a captioned showcase —.
Explore the method : Video production and caption editingCreate platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.
Explore the method : Video scriptwritingCreate professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.
Explore the method : Visual communication briefDesign, build, revise, or QA quantitative charts and figures. Use when an analytical answer needs visual judgment, whether for an inline answer, report, dashboard, notebook, or artifact.
Explore the method : Visualize DataWeighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.
Explore the method : Warm introduction planningReview trading records for potential wash-sale patterns under a specified jurisdiction, tax year, and verified current instrument rules. Source crypto tax claims are historical and require authoritative verification. Produce candidate adjustments for review; do not assume every crypto disposition is subject to a static 61-day rule.
Explore the method : Wash Sale DetectionAuto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target
Explore the method : Waste analysisReview UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".
Explore the method : Web design guidelinesGenerate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation. Trading analysis or dry-run only; live data and broker access require separately available host tools.
Explore the method : Weekly Performance DigestCreate optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Explore the method : Workflow designReview docs/prose for Writing Guidelines compliance. Use when asked to "review my docs", "check writing style", "audit prose", "review docs voice and tone", or "check this page against the writing handbook".
Explore the method : Writing guidelinesGrilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate", or "raw material" for writing.
Explore the method : Writing ideationUse when you have a spec or requirements for a multi-step task, before touching code
Explore the method : Writing PlansUse when creating new skills, editing existing skills, or verifying skills work before deployment
Explore the method : Writing SkillsX (Twitter) data extraction and scraping. Use when asked to scrape tweets, extract followers, search Twitter/X users, download media from tweets, monitor X accounts, or analyze Twitter engagement. Triggers on twitter, x.com, tweet, follower, following, retweet, quote tweet, scrape, OSINT.
Explore the method : X social researchUse this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Explore the method : XLSXRead, fetch, or extract a YouTube video's transcript, captions, subtitles, or spoken text from a watch, Shorts, or youtu.be link. Use for reading a video, timestamped notes, or a transcript-based summary and questions. Also accepts pasted transcripts and SRT/VTT files. Returns Markdown grounded in the actual captions; video creation and caption rendering belong elsewhere.
Explore the method : YouTube transcript readerUse when using Apps SDK.
Explore the method : Zoom Apps SDKUse when using Cobrowse.
Explore the method : Zoom Cobrowse SDKZoom Contact Center SDK for Android. Use for native Android chat/video/ZVA/scheduled callback integrations, campaign mode, service lifecycle, and rejoin handling.
Explore the method : Zoom Contact Center AndroidZoom Contact Center SDK for iOS. Use for native iOS chat/video/ZVA/scheduled callback integrations, app lifecycle bridging, rejoin flow, and callback handling.
Explore the method : Zoom Contact Center IosZoom Contact Center SDK for Web. Use for web chat/video/campaign embeds, engagement event handling, app-context integrations, and Smart Embed postMessage workflows.
Explore the method : Zoom Contact Center WebUse when comparing products.
Explore the method : Zoom GeneralZoom Meeting SDK for Android native apps. Use when embedding Zoom meetings in Android with default/custom UI, PKCE + SDK auth, join/start flows, and Meeting SDK API integration.
Explore the method : Zoom Meeting SDK AndroidZoom Meeting SDK for Electron desktop applications. Use when embedding Zoom meetings in an Electron app with the Node addon wrapper, JWT auth, join/start flows, settings controllers, and raw data integration.
Explore the method : Zoom Meeting SDK ElectronZoom Meeting SDK for iOS native apps. Use when embedding Zoom meetings in iOS with default/custom UI, PKCE + SDK auth, host start with ZAK, and mobile lifecycle handling.
Explore the method : Zoom Meeting SDK IosZoom Meeting SDK for Linux - C++ headless meeting bots with raw audio/video access, transcription, recording, and AI integration for server-side automation
Explore the method : Zoom Meeting SDK LinuxZoom Meeting SDK for macOS native apps. Use when embedding Zoom meetings in macOS with default/custom UI, PKCE + SDK auth, host start/join flows, and desktop meeting feature controllers.
Explore the method : Zoom Meeting SDK MacosZoom Meeting SDK for React Native. Use when embedding Zoom meetings in React Native iOS/Android apps with @zoom/meetingsdk-react-native, JWT auth, join/start flows, platform setup, and native bridge troubleshooting.
Explore the method : Zoom Meeting SDK React NativeZoom Meeting SDK for Unreal Engine wrapper integrations. Use when building Unreal projects that embed Zoom meetings with C++ and Blueprint wrappers, including wrapper-to-SDK mapping concerns.
Explore the method : Zoom Meeting SDK UnrealZoom Meeting SDK for Web - Embed Zoom meeting capabilities into web applications. Two integration options: Client View (full-page, familiar Zoom UI) and Component View (embeddable, Promise-based API). Includes SharedArrayBuffer setup for HD video, gallery view, and virtual backgrounds.
Explore the method : Zoom Meeting SDK WebZoom Meeting SDK Web - Client View. Full-page Zoom meeting experience with the familiar Zoom interface. Uses ZoomMtg global singleton with callback-based API. Ideal for quick integration with minimal customization. Provides the same UI as Zoom Web Client.
Explore the method : Zoom Meeting SDK Web Client ViewZoom Meeting SDK Web - Component View. Embeddable Zoom meeting components with Promise-based API for flexible integration. Ideal for React/Vue/Angular apps and custom layouts. Uses ZoomMtgEmbedded with async/await patterns and embeddable UI containers.
Explore the method : Zoom Meeting SDK Web Component ViewZoom Meeting SDK for Windows - Native C++ SDK for embedding Zoom meetings into Windows desktop applications. Supports custom UI architecture with raw video/audio data, headless bots, and deep integration with meeting features. Includes SDK architecture patterns and Windows message loop handling.
Explore the method : Zoom Meeting SDK WindowsUse when implementing OAuth.
Explore the method : Zoom OauthUse when using RTMS.
Explore the method : Zoom RtmsZoom Video SDK for Android native apps. Use when building custom Android video experiences with full UI control, session tokens, raw media options, and event-driven participant state.
Explore the method : Zoom Video SDK AndroidZoom Video SDK for Flutter. Use when building custom video session apps in Flutter with flutter_zoom_videosdk, event-driven architecture, session lifecycle handling, and mobile platform integration patterns.
Explore the method : Zoom Video SDK FlutterZoom Video SDK for iOS native apps. Use when building custom iOS video sessions with full UI control, token-based session auth, and event-driven media/participant flows.
Explore the method : Zoom Video SDK IosZoom Video SDK for Linux - C++ headless bots, raw audio/video capture/injection, Qt/GTK integration, Docker support
Explore the method : Zoom Video SDK LinuxZoom Video SDK for macOS native desktop apps. Use when building custom macOS video sessions with native UI control, tokenized join, and desktop-oriented media/device workflows.
Explore the method : Zoom Video SDK MacosZoom Video SDK for React Native. Use when building custom mobile video session experiences with @zoom/react-native-videosdk, event listeners, helper-based APIs, and backend JWT token flows.
Explore the method : Zoom Video SDK React NativeZoom Video SDK for Unity wrapper integrations. Use when building custom Unity-based video session experiences and mapping Unity scene/UI state to Video SDK events.
Explore the method : Zoom Video SDK UnityZoom Video SDK for Web - JavaScript/TypeScript integration for browser-based video sessions, real-time communication, screen sharing, recording, and live transcription
Explore the method : Zoom Video SDK WebZoom Video SDK for Windows - C++ integration for video sessions, raw audio/video capture, screen sharing, recording, and real-time communication
Explore the method : Zoom Video SDK WindowsZoom Virtual Agent Android integration via WebView. Use for Java/Kotlin bridge callbacks, native URL handling, support_handoff relay, and lifecycle-safe embedding.
Explore the method : Zoom Virtual Agent AndroidZoom Virtual Agent iOS integration via WKWebView. Use for Swift/Objective-C script injection, message handlers, support_handoff relay, and URL routing policies.
Explore the method : Zoom Virtual Agent IosZoom Virtual Agent SDK for web embeds. Use for campaign or entry ID chat launch, event-driven controls, user context updates, and CSP-safe deployment.
Explore the method : Zoom Virtual Agent WebUse Zotero Desktop from Codex to enable/probe the local API, search a local Zotero library, list items/collections/tags, export BibTeX, insert citation keys into LaTeX or Markdown drafts, read indexed full text when requested, and import BibTeX/RIS records into Zotero through the connector server. Use when the user mentions Zotero, citations, references.bib, BibTeX export, local Zotero API, localhost:23119, or adding citations from a Zotero library.
Explore the method : Zotero