リサーチ・学習のスキル

Audit and maintain the learnings in `docs/solutions/` against the current codebase — fix drifted paths and links, merge overlapping docs, replace guidance the code no longer supports, delete obsolete docs, reconcile the `CONCEPTS.md` glossary, then report and commit. Also bootstraps `CONCEPTS.md` on request and, when asked, culls docs whose reasoning the code already explains. Use for "refresh the learnings", "audit docs/solutions", "are our solution docs still accurate", "clean up / prune the learnings", "create a CONCEPTS.md", or after a refactor, rename, migration, or dependency upgrade that likely invalidated documented solutions. Not for capturing a new learning — use `learn-with-docs`.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1; route continuous learning, session learning, and pattern extraction requests to continuous-learning-v2.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Competitive benchmarks, solution research, and hypothesis generation for identified bottlenecks

日本語の概要は準備中です。原文の説明を表示しています。

Multi-cycle diagnostic pipeline: profile → research → optimize → repeat with full-stack bottleneck analysis

日本語の概要は準備中です。原文の説明を表示しています。

Discovers custom modules (>100 LOC), analyzes purpose, searches OSS alternatives via MCP Research, evaluates quality (stars, license, CVE), generates migration plan.

日本語の概要は準備中です。原文の説明を表示しています。

Persistent cross-session knowledge base. Manages per-project learnings that compound across sessions — patterns, pitfalls, preferences, and project quirks. The agent gets smarter on your codebase over time. At session start, searches relevant learnings. During sessions, logs discoveries. Supports review, search, prune, and export. Use proactively when the agent encounters a durable project quirk, user preference, or pattern that would save 5+ minutes next time. Also use at session start to recover context. Triggers: "remember this", "log this", "what have we learned?", "show learnings".

日本語の概要は準備中です。原文の説明を表示しています。

Research current job markets and hiring requirements, analyze recent job descriptions (JDs), compare them with a user's resume, projects, skills, and constraints, and turn evidence gaps into prioritized career actions. Use when students, job seekers, or career changers ask about current role or industry outlook (including “现在行情怎么样”), whether a role or JD fits them (including “这个JD适合我吗”), cross-platform JD sampling, resume evidence gaps or truthful tailoring, what career skill to learn next, target-role course or certificate decisions, application readiness, or a capacity-bounded 30/60/90-day job-search plan. Do not use for unrelated personal or education advice, simple factual lookups, automatic job applications, or unsupported resume rewriting.

日本語の概要は準備中です。原文の説明を表示しています。

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

日本語の概要は準備中です。原文の説明を表示しています。

Scientific research engine with agentic tree search. Infinite loops until discovery, rigorous tracking, adversarial review, serendipity preserved.

日本語の概要は準備中です。原文の説明を表示しています。

Scientific research in serendipity mode. Infinite loops until discovery, rigorous tracking, adversarial review.

日本語の概要は準備中です。原文の説明を表示しています。

Develop interactive PaperJS applications for visual drawing, animation, and vector graphics. Create canvas-based experiences with JavaScript and PaperJS framework.

日本語の概要は準備中です。原文の説明を表示しています。

Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.

日本語の概要は準備中です。原文の説明を表示しています。

Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT for library/framework docs (use Context7) or workspace questions.

日本語の概要は準備中です。原文の説明を表示しています。

Identify high-quality sales leads by analyzing your business, defining ideal customer profile, and discovering target companies with personalized contact strategies.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze recent code changes via git history and extract software engineering lessons. Use when the user asks 'what is the lesson here?', 'what can I learn from this?', 'engineering takeaway', 'what did I just learn?', 'reflect on this code', or wants to extract principles from recent work.

日本語の概要は準備中です。原文の説明を表示しています。

Collaborative writing partner that assists with research, outlining, drafting, citations, and iterative feedback. Helps writers create high-quality content while maintaining their unique voice and style.

日本語の概要は準備中です。原文の説明を表示しています。

Automatically fetch latest library/framework documentation for Claude Code via Context7 API. Use when you need up-to-date documentation for libraries and frameworks or asking about React, Next.js, Prisma, or any other popular library.

日本語の概要は準備中です。原文の説明を表示しています。

Pedagogical patterns for enriching notebooks (GameTheory model). Use when adding interpretations, structuring notebooks, or creating educational content in Jupyter notebooks.

日本語の概要は準備中です。原文の説明を表示しています。

Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.

日本語の概要は準備中です。原文の説明を表示しています。

Integration of Kahneman's heuristics research with Klein's expertise studies on intuitive decision-making

日本語の概要は準備中です。原文の説明を表示しています。

Naturalistic decision-making research showing how experts make decisions through recognition-primed processes

日本語の概要は準備中です。原文の説明を表示しています。