调研、论文、教育、考证学习（4827 个）
Use when the user names interviews, user research, a survey, a beta, or a pile of feedback to make sense of: the interview plan with a story opener, or ranked themes led by verbatim quotes with a roadmap verdict on each.
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Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.
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Run cold outreach that gets replies: research the account, write a short specific first message, and follow up on a fixed schedule without nagging.
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Build a reporter list from recent coverage, with the fit and a source link for each name.
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Use on the first chat with Max, or whenever their memory has no sales preferences yet: learn the book they sell into and put one real read in front of them.
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Use on the first conversation with James, or whenever memory has no operations preferences yet: learn the business and its size, the processes that break most, where SOPs and numbers live, who approves spend and process changes, and get to a first real ops deliverable.
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Build a sourced investor target list from published stage, sector, geography, cheque range, portfolio, and conflict facts.
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Use when a researched prospect row needs its opening message by email, social, phone, or short public post.
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Use for how-to questions, repeat issues, and onboarding guidance: docs-first answers with citations, plus the saved reply or KB entry the next ticket reuses.
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Use on the first conversation with Daniel, or whenever no finance preferences are saved yet: learn the revenue model, the books, the thresholds that count as too far, and where the numbers live, then get to a first read.
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When 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.
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Multi-source research on a customer question or topic with source attribution. Use when a customer asks something you need to look up, investigating whether a bug has been reported before, checking what was previously told to a specific account, or gathering background before drafting a response.
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Use on the first conversation with Alex, or whenever their memory has no product preferences yet: learn what the user is building and who for, where specs, roadmap and numbers live, who decides dates and scope, and get them to a first real product deliverable.
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Use on the first conversation with Anika, or whenever their memory has no partnership preferences yet: learn which partners and alliances the user owns, what motion they run, and get one real partner read on screen in the same session.
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Researches a question to something usable — a verdict with a flip condition, a versioned playbook or a graded report — every claim tagged by evidence. Trigger on which X should I pick (even from pasted pages), is Y worth it, compare A vs B, go/no-go, research this, deep research, fact-check a doc, write or merge a reference guide or playbook, grade a research report, read a bot-blocking source (Reddit, YouTube, X), or map an unfamiliar repo before contributing (entry points, flows, where to change X); or say /rw-researchscribe (verdict, playbook, research, verify, sources, map, refresh). Code review is engineering tooling's; fiction canon is lorescribe's; messages commscribe's; prompt model picks promptwright's; skill niches skillwright's; config rigwright's; brands brandscribe's.
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英語の論文（特にロボティクス／Physical AI／機械学習）を日本語で精読解析し、英日対訳2カラムのHTMLノートを生成する。Keshavの3パス法＋Andrew Ngの4つの問い＋Jennifer Raffの精読法を統合したメソッドで、専門用語は英語＋日本語解説を併記する。arXiv URL・PDFファイル・論文タイトルのいずれからでも起動。「この論文を解析して」「論文を日本語でまとめて」「精読ノートを作って」等で使う。
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status:needs-investigation な issue を並列調査し、結果をコメントして status:investigated に更新する
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status:investigated な issue の調査結果コメントを読み、実装可能なら status:ready に昇格させる
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Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
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Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.
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Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
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Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
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Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
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Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
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