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Found 204 Skills
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
Expert product strategy and product marketing skill. Use when defining product vision, assessing product-market fit, sizing market opportunities, doing competitive positioning, building growth loops, designing PLG strategies, creating product marketing context, or using the Working Backwards methodology. Activates for: product strategy, product marketing, PMM, product manager, product management, growth product manager, working backwards, PR/FAQ, Amazon PR FAQ, product roadmap, product positioning, product-market fit, product launch, feature prioritization, TAM SAM SOM, market sizing, competitive moat, business model design, monetization strategy, north star metric, activation, retention, growth loops, freemium, PLG, product-led growth, growth experimentation, ICP context, marketing context document.
Expert methodology for analyzing and summarizing research papers, extracting key contributions, methodological details, and contextualizing findings. Use when reading papers from PDFs, DOIs, or URLs to create structured summaries for researchers.
Boîte à outils complète pour la manipulation de PDF : extraction de texte et tableaux, création de nouveaux PDF, fusion/découpage de documents et gestion de formulaires. Quand Claude doit remplir un formulaire PDF ou traiter, générer ou analyser des documents PDF de manière programmatique et à grande échelle.
Token-efficient persistent memory system for Claude Code that extends your session limits by 3-5x. Layered architecture with progressive loading, compact encoding, branch-aware context, smart compression, session diffing, conflict detection, session continuation protocol, and recovery mode. Activates at session start (if MEMORY.md exists), on "remember this", "pick up where we left off", "what were we doing", "wrap up", "save progress", "don't forget", "switch context", "hand off", "memory health", "save state", "continue where I left off", "context budget", "how much context left", or any session start on a project with existing memory files. This skill solves two problems at once: Claude forgetting everything between sessions, AND sessions hitting context limits too fast. It replaces thousands of wasted re-explanation tokens with a compact, structured memory load that gives Claude full project context in under 2,000 tokens.
Test quality review drawing on twelve classic engineering books — with primary focus on xUnit Test Patterns, The Art of Unit Testing, How Google Tests Software, and Working Effectively with Legacy Code — that diagnoses structural problems in an existing test suite: brittleness, mock abuse, coverage illusions, slow execution, poor readability. Triggers when: user asks about test quality, shares test files for review, or expresses frustration: "tests keep breaking whenever I change anything", "our tests take forever", "I can't understand what this test is doing", "tests pass but bugs still reach production", "we have too many mocks". Do NOT trigger for: writing new tests from scratch (use the regular test-writing workflow) or testing framework/syntax questions — this skill reviews an existing suite for structural quality problems, not individual test authoring.
Explain how claude-mem captures observations, when memory injection kicks in, and where data lives. Use when the user asks "how does claude-mem work?" or "what is this thing doing?".
Search Web of Science by topic, author, title, DOI, or advanced query. Supports edition/database filtering and sort.
Use when normalizing BibTeX, RIS, CSL JSON, citation keys, DOI/arXiv/PMID metadata, references, unused citations, missing citations, or bibliography quality for papers and SOTA work.
Trace a file, function, or line back to the agent session that produced its current commit. Use when the user asks "why is this code here", "what was the agent doing when this changed", or wants context on a specific location in the codebase.
Use when doing dev-stage self-review on the current branch before pushing or opening a PR — runs an auto-loop of codex review (cross-model, OpenAI) + per-finding fix + re-review until findings converge or stop conditions fire. Codex follows pr-review's multi-role methodology (security / staff-engineer / sdet / spec-auditor). Triggers — 'self review', 'self-review', '自己 review', '自我 review', 'cross-model review', 'pre-push review', 'review and fix my branch'. NOT for live PR review with sticky/inline comments (use pr-review), NOT for managed PR babysitting (use pr-babysit), NOT for first-time review without intent to fix (use mode=review-only opt-in).
THE workflow for picking up and carrying ONE ticket/card forward, for an autonomous worker agent or for a human doing it locally. Resolves the repo's tracker from the AFK registry (~/.claude/afk.json; GitHub Projects or Linear), picks one ticket by priority, routes by status x label (interview / human walkthrough / execute), loads LEARNINGS.md as binding constraints, implements test-first, verifies end-to-end and simplifies the diff (the /go finish), then branches to a PR for the reviewer. Use when the user says "pick up <id>", "work on issue <id>", invokes /engineer, invokes /pickup, says "pickup", or at the very start of working any card.