Research, academic papers, education, and exam prep (5K skills)
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
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Structured interview and observation techniques for extracting tacit knowledge from domain experts
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Adversarial reviewer for The Federated Harbor whitepaper — the third paper in the Curiositech sequence after Anchor and Bonded Commons. Probes federation-specific threats: trust transitivity, cross-harbor token forgery, federated revocation under partition, cross-harbor Sybil, cross-domain settlement, equivocation, bond-pool draining, cold-start joining, federation-operator Sybil. Use during a versioned red-vs-white round; pairs with federated-harbor-whitehat. NOT for incident response, NOT for the Anchor or Bonded papers (use redteam-review).
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Analyze undirected cycle structure within a valid DAG using the source paper's orientation and contraction method. Use for cycle-basis analysis with explicit graph and reduction choices. NOT for directed feedback loops, workflow deadlocks, or runtime resilience claims.
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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.
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Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).
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Conduct Critical Decision Method (CDM) interviews to elicit expert reasoning strategies (L3 knowledge) from practitioners about specific challenging past incidents. Use when capturing expertise for skill development, preserving knowledge before practitioner departure, building post-incident learning artifacts, or investigating decisions in complex sociotechnical systems. Outputs structured incident reconstructions with cues, decisions, alternatives considered, and counterfactual analysis suitable for distillation into skills or training material.
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Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.
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Revise the Harbor Book so prose, drawings, and marginalia explain the same argument without unpublished development-document dependencies. Use for chapter additions, substantive rewrites, caption editing, visual exposition, and eliminating internal ADR citations.
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Apply AlphaGo-style patterns: learned priors plus search, mixed evaluators, self-play, and asynchronous fast/slow loops. Use for intractable search and compute allocation. NOT for basic supervised learning, tractable optimization, or domains without search bottlenecks.
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Research AI coding assistant products, audiences, user stories, social proof, hacks, and unmet needs for apps like Cursor, Claude Code, Codex, Warp Code, Devin, Windsurf, GitHub Copilot, Cline, Aider, and homegrown agent stacks. Use when designing or positioning agentic developer tools, Port Daddy Agent Harbor, swarm consoles, background coding agents, or competitive/product research for AI software engineering. NOT for implementing the coding agent runtime, generic market research, or UX mockups without developer workflow evidence.
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Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.
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Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.
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Generates creative, research-backed domain name ideas and checks availability with competitor analysis, pattern recognition, and quality scoring. Use when brainstorming brandable domains for a product or project.
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実行中・実行済みのプログラムやジョブの状況を知りたいとき，自分が起動したものか無関係のものかを問わず調べて答える．「どうなってる？」「学習状況は？」「進捗見せて」など，実行そのものを頼んでいるわけではなく既存の何かの様子を尋ねられたときに使う．新しく実行を頼まれたときは /ops:exec を使う．
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ユーザーからの修正・指摘を受けた際の学習記録と自己改善ループ。ユーザーから「違う」「そうじゃない」「前にも言ったけど」といった修正を受けた時、同じミスを繰り返していると気づいた時、セッション開始時の教訓確認で使う。修正やフィードバックを受けたら必ずこのスキルを参照する。
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repositoryを調査し、`docs/PLAN/YYMMDD_{slug}.md` にPhase、Gate、完了状態、実行ルール、チェック項目を持つ自己完結した永続計画を作成または再構成する。Use when the user asks for simple-plan、チェックリスト式の計画、複数停止点を持つ中規模作業、段階的なapp分割やlocal migration、production前Gate、または既存planの構成変更。単発Sprintでは収まらないが、詳細なworker委任計画までは不要な作業向け。既存planをそのまま実行・再開するだけの場合は使わない。
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HeroUI Pro React の新リリースを公式サイトから調査し、 指定されたリリースノートドキュメントを追記更新するスキル。 「できるようになったこと・作れるようになったもの」を主軸に、 自然な日本語でユーザーストーリーとして記述する。 Trigger: HeroUI Pro リリースノート更新, heroui-pro release notes, heroui-pro更新, /dev:heroui-pro-release-notes
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コーディング前のリサーチワークフロー。カスタムコードを書く前に既存のツール、ライブラリ、パターンを検索する。リサーチャーエージェントを起動。
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市場調査、競合分析、投資家デューデリジェンス、業界インテリジェンスをソース帰属と意思決定指向のサマリーとともに実施。市場規模推定、競合比較、ファンドリサーチ、テクノロジースキャン、ビジネス意思決定に活用するリサーチに使用。
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品質管理、不適合調査、根本原因分析、是正措置、サプライヤー品質管理に関する 体系化された専門知識。規制下の製造環境で15年以上の経験を持つ品質エンジニアの 知見に基づく。FDA、IATF 16949、AS9100環境を網羅。NCRライフサイクル管理、 CAPAシステム、SPC解釈、監査方法論を含む。不適合の調査、根本原因分析、 CAPA管理、SPCデータの解釈、サプライヤー品質課題の対応に使用。
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firecrawlとexa MCPを使用したマルチソースディープリサーチ。Webを検索し、調査結果を統合し、ソース帰属付きの引用レポートを提供。トピックについてエビデンスと引用付きの徹底的なリサーチが必要な場合に使用。
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フックを介してセッションを観察し、信頼度スコアリング付きのアトミックなインスティンクトを作成し、それらをスキル/コマンド/エージェントへ進化させるインスティンクトベースの学習システム。v2.1ではプロジェクトスコープのインスティンクトを追加し、プロジェクト間の汚染を防止します。
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Claude Codeセッションから再利用可能なパターンを自動的に抽出し、学習済みスキルとして保存。
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