執筆・創作のスキル

Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3/SQANTI-LR (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.

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Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

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Use when a carrier asks about the full pre-employment investigation process for new CDL drivers under 49 CFR 391.23, 391.25, 391.27, 382.413, and 391.21 — the complete file build required before a driver operates a CMV. Covers application, MVR, employment history, drug/alcohol history, PSP, Clearinghouse, road test, and documentation requirements.

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Use when a carrier asks about autonomous trucks — current state of Level 4 development, major programs (Aurora, Kodiak, Plus, Waymo Via, Embark history), regulatory status, operational implications, hub-to-hub model, driver impact, insurance, and what carriers should be doing now (or not doing) to prepare.

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Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).

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Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).

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Use when applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.

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Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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Audit whether an enforced-coordination MCP has actually shrunk its ~19 legacy etiquette verbs into the 5 enforced tools — work/act/ask/recall/status — each carrying one denial shape and one transcript event, migrated through a real retirement path (intake-metadata, alias, or doc-history — never a parallel runtime), with zero legacy-verb calls once a body reaches compliance mode C4+. Use when reviewing a broker-collapse migration plan, gating a body's move from advisory (C0-C3) to enforced (C4+) coordination, or verifying IT-018 Broker Collapse compliance. NOT for the inbound trigger→spawn boundary that decides whether an event may spawn an agent at all (use fleet-event-spawn-trust), the capability-token cryptographic mechanism behind a denial (use agentic-zero-trust-security), designing how a swarm of agents invokes and shards work (use swarm-invocation-designer), or auditing an individual PR's mergeability (use agent-pr-authoring).

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Architecture and systems design for agents that retain bounded state across sessions. Covers durable event history, owned state, derived memory, retrieval indexes, framework evaluation, recovery, deletion, and rebuild behavior. Uses constructed implementation examples and workload calibration rather than universal vendor, latency, similarity, retention, or cost claims. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).

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Derive a SET of simulation personas for an agent from product artifacts — backend payloads, UI screenshots, journey/product docs, and sample real user messages — instead of designing one persona by hand. Identifies who actually interacts with the agent and how they behave, then creates the personas via the CLI. Best for text/chat agents and for new agents with no interaction history. Use when the user says "make personas from these screenshots/payloads", "who are my users", "create a set of personas", "derive personas from my product", "build a persona library", or "I have backend data, turn it into personas".

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Automatically creates user-facing changelogs from git commits by analyzing history, categorizing changes, and transforming technical entries into customer-friendly release notes. Use when preparing release notes from commit logs.

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Write content in David Dias's authentic voice. Use when Quill or any agent needs to create tweets, blog posts, emails, marketing copy, or any content that should sound like David wrote it. David's voice is personal and conversational, mixing professional insight with personal anecdotes. He shares failures openly, values community, and speaks directly to readers with honesty and warmth. Not corporate or overly polished.

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Marp用Markdownプレゼンテーションファイルの作成・編集。スライドコンテンツの執筆、frontmatter設定、ディレクティブ適用を支援。「スライドを作りたい」「プレゼンを作成」などのリクエスト時に使用。

課題のレポート・小論文部分を，出題文・採点基準の要求を過不足なく満たす形で執筆する．「レポートを書いて」「この設問に答えて」など，課題文書の執筆を求められたときに使う（既存文書の推敲だけなら /refine:docs を使う）．

This skill should be used when the user asks to "ドキュメントを更新して", "sync the docs", "update CLAUDE.md after this change", "実装に合わせてドキュメントを直して", or notices that docs have drifted from the codebase. Diffs git history to identify what changed, proposes targeted updates to CLAUDE.md, docs/architecture.md, docs/requirements.md, and README.md, then applies only approved changes.

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プロダクトアイデアをJTBD分析→競合調査→SLC仕様書に構造化。 dev:storyの上流で使用し、何を作るべきかを明確にする。 「アイデアを整理」「/dev:ideation」で起動。 Trigger: アイデア整理, プロダクト企画, /dev:ideation, ideation, 何を作るべきか

ユーザーストーリーの追加・精緻化と、US IDを指定した計画・実装・レビュー・Journey検証を行う。台帳を製品契約の正本とし、Gateと開発状態を管理する。

PR、Story、PLAN、実装成果の初回レビューと再レビューで、現在契約と到達可能な影響範囲からGO／NO-GOを判定する。実装や自由な改善探索には使わない。

フィーチャー全体の設計・ストーリー分割を行い、PLAN.md と plan.json を生成。 ストーリー駆動開発フローと統合し、dev:story の上位レイヤーとして機能する。 各ストーリーに executionType とフェーズを付与し、実行計画を構造化する。 Trigger: フィーチャー計画, エピック作成, 全体設計, /dev:epic

ストーリーからTDD/E2E/TASK分岐付きタスクリスト（task-list.json）を生成。 ストーリー駆動開発の起点となるスキル。 「タスクを作成」「/dev:story」で起動。 Trigger: タスクを作成, ストーリーからタスク, /dev:story, story to tasks