調査、論文、教育、資格学習（4,456 件）
Produce customer case studies from interviews or data — Problem, Solution, Results structure with perception engineering and quality gates. Use when "case study", "customer story", "testimonial", "success story", "client results", or any request to document a customer win.
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Analyze content performance patterns. Extract what works (hook types, formats, personas). Auto-update learned defaults so future content improves.
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Write content using the most recent brief. Loads framework rules, persona hooks, learned patterns, and skill contract. Auto-runs quality gate on completion.
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Multi-source comprehensive research using perplexity-researcher, claude-researcher, and gemini-researcher agents. Launches up to 10 parallel research agents for fast results. USE WHEN user says 'do research', 'research X', 'find information about', 'investigate', 'analyze trends', 'current events', or any research-related request.
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Convert deep research output (Claude Research, ChatGPT Deep Research, Gemini, Codex — any long-form MD/Word/text) into a consulting-quality HTML report: pyramid structure, numbers promoted to KPIs/charts, comparisons turned into framework tables, citations preserved. Use when the user provides research/investigation results and says レポートにして/HTMLにまとめて/資料にして/きれいにして, mentions ディープリサーチの結果/調査結果/リサーチ結果, or asks to research a topic AND deliver a report. NEVER invents numbers or facts not present in the source (盛らない原則).
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Use when 高教社杯/CUMCM mathematical modeling papers need lightweight literature search, citation verification, method/background references, standards or policy sources, Chinese-English source routing, DOI/BibTeX checks, or claim-to-citation mapping without fabricated sources.
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Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.
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Working memory protocol — maintains context across sessions via CONTINUITY.md. Inspired by Loki Mode. Read at turn start, update at turn end. Captures mistakes and learnings to prevent repeating errors.
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World-class practice research. This skill should be used when the user asks what the best in the world does about a specific problem, technique, or situation — covering product, engineering, design, marketing, compliance, operations, org design, and any other domain. Use when the goal is to understand elite-tier practice, not to make a strategic decision. Distinct from best-in-world-strategy, which is for choosing between options.
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Cross-disciplinary polar Earth-system manuscript review orchestrator for Arctic, Antarctic and Southern Ocean research. Routes claims to existing domain reviewer skills and applies polar-specific evidence gates for sparse observations, cryosphere variables, product dependence, conservation budgets, coupled attribution, paleoclimate archives and regional research responsibility.
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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.
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Use when the user asks a question about how things in the codebase or wiki relate — what connects two things, how something works end to end, what depends on what, or what a concept means in this repo. Answers from the knowledge graph in graphify-out/graph.json, with source-location citations. Trigger on "/graph-query", "ask the graph", "what connects X to Y", "trace how X works", "explain <concept> from the graph", "shortest path between".
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Use to run an autoresearch-style experiment loop — improve a measurable artifact by trying changes, measuring against one objective metric, keeping if better and reverting if not, on an isolated git branch with a logged trail. Trigger on "/experiment", "run an experiment loop", "autoresearch X", "tune/optimize X against a metric", "hill-climb X". Follows references/sops/experiment-loop.md. Enforces the autonomy gate (objective metric + reversible sandbox + no PHI/external) before any unsupervised looping.
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Use for cheap parallel web discovery (gpt-5.6-luna). Triggers: Luna search, parallel research, 루나검색, 루나 서치, 병렬 웹검색, 싸게 많이 찾아봐.
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Session lifecycle management. Parent skill for session-related skills: learning (pattern extraction) and compact (context compression).
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Extracts reusable patterns from sessions. Use at session end to capture debugging insights and project-specific knowledge.
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Research-before-coding workflow: systematically search existing solutions before custom implementation. Use when introducing new libraries, tools, or solving unfamiliar problems.
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Use when starting a new project. Genesis finds real production failures from similar GitHub repos, then builds a working MVP immediately - not just documents. Two modes: fast-mvp (5 min research cap, builds right after) and deep-research (full analysis). Success = user can run the project. Triggers on: "genesis init [vision]", "I want to build X", "scaffold", "new project", "set up project", "start building", "create a tool", "make a CLI", "bootstrap", "בנה פרויקט", "צור פרויקט", "התחל פרויקט".
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Search the ClinicalTrials.gov registry through its version 2 REST API for interventional and observational studies, their phases, enrolment, endpoints, sponsors, and posted results. Use this skill to survey who is developing what against an indication, date a competitor's programme, read primary and secondary outcome measures, find eligibility criteria, and distinguish a study that completed from one that was terminated or withdrawn. Also trigger on ClinicalTrials.gov, NCT number, trial registry, study phase, enrolment, primary outcome measure, recruiting status, trial sponsor, or competitive landscape.
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Checks construction drawings and specs for what's missing, cross-trade coordination conflicts and scope gaps, with PE judgment. Use for 'what's missing', 'check coordination', 'scope gap', or researching an RFI before answering it. Not for drafting RFIs (rfi-drafter), code research (code-researcher) or reviewing a submittal package (the user runs /construction:submittal-review).
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Researches which building codes, accessibility standards and authority requirements (health department, fire marshal, state agency) apply to a scope or spec section, and which the project documents miss. Use for 'what codes apply', 'code check', 'ADA requirements', 'egress', 'health department'. Not for reading what the drawings or specs say (construction-guide).
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深挖一组 subreddit 的"社区研究"技能。给一个品牌或项目,先摸清相关(自家品牌+竞品)的 subreddit:爬规则原文、爬高赞帖逐帖学、找共性、产出"能不能提品牌/能不能发链接/要不要披露"的逐社区政策矩阵。用于任何新品牌/新项目上 Reddit 前的社区尽调。触发词:"研究 subreddit""摸清社区规则""Reddit 社区尽调""subreddit deep research""某品牌相关的 sub"。
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Central dispatcher for the canvas/state render fleet. Routes intent to a specialist (`/mycelium:diamond-render`, `/mycelium:ost-render`, `/mycelium:cycle-render`) OR lists what's renderable. Cross-cutting views spanning multiple canvases (opp→sol→cycle traceability, confidence-over-time, cluster-graduation-flow) are research-gated and NOT emitted by default. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared conventions.
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Structured retrospective after completing a delivery increment or diamond. Captures learning for continuous improvement.
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