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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).

Search markdown knowledge bases, notes, and documentation using QMD. Use when users ask to search notes, find documents, or look up information.

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。

Enforce the project's documentation voice when writing or reviewing public-facing content. Triggers on edits to doc files (packages/web/src/content/docs/**/*.md) and README.md. Catches LLM prose patterns, marketing language, and messaging drift.

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。

The Mileva Method (CRISP) — Phase 4: Spec. Full implementation readiness package including solution design, UX direction, tech stack, backlog, MVP prioritization, risk assessment, and AI architecture for Claude Code projects — CLAUDE.md, skill mapping, AI specs, sprint planning, quality gates. Triggers on "spec", "phase 4", "build ready", "implementation plan", "CLAUDE.md", "sprint planning", "backlog", "AI spec", or after Phase I exit checklist is complete.

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Knit/render R Markdown (.Rmd) to HTML reliably in this repo (auto-bootstrap pandoc, set correct knit_root_dir, and run rmarkdown::render via a Python wrapper).

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当主要交付物是基于 R 的数据分析流程、科学结果、可复现数据产品、R Markdown/HTML 报告或论文级图表与解读时使用；即使流程中包含 `.R` 脚本和仅服务当前分析的辅助函数，也由本 Skill 主导。⚠️ 不适用：主要交付物是可独立复用的 R 函数、稳定公共 API、类或 R Package；这些任务使用 bensz-r-developer。也不用于仅渲染既有 Rmd 或其它语言的数据分析。

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Babel internal issue protocol — close a handoff after downstream agent picked it up or user signed off. Moves designs/<name>/.handoff/<label>.md to .handoff/closed/ (using mv per CLAUDE.md), appends to handoff_log.jsonl.

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Use to run a dynamic, goal-driven workflow — orchestrate a multi-step goal by composing skills, tools, and MCPs, with a quality gate and verification at each step. Trigger on "/workflow", "run the <name> workflow", "orchestrate X", or a multi-step recurring goal ("draft the investor update", "give me my morning brief"). Follows references/sops/dynamic-workflows.md and the wf-*.md cards. Respects the autonomy/cadence gate (auto on reversible/internal, draft-then-approve on external/regulated).

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。

Apply whenever writing or updating a project-facing document — root CLAUDE.md, `.hedgehog/core-design.md`, specs, READMEs, or similar. Ensures the file reads as a clean, current, as-is snapshot rather than a log of edits or decisions. Applies on first generation and on every later revision, including "update," "rewrite," or "clean up" requests.

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。

Capture the user's UX vision in two documents: DESIGN.md for how the product looks and EXPERIENCE.md for how it behaves. Use when the user says "lets create UX design" or "create UX specifications" or "help me plan the UX"

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Analyzes a Stitch project's screens and synthesizes a natural-language DESIGN.md — visual atmosphere, color palette with hex values, typography rules, and Stitch-ready prompt snippets. Use this before stitch-loop or any multi-page build to establish design consistency.

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Use when writing, moving or auditing any .md in imp - deciding which file a paragraph belongs in, adding a doc, fixing a stale claim, or when docs_lint.py fails in CI. Covers the four reader layers (L0 README / L1 operators / L2 kernel devs / L3 agents), the HTML-comment metadata header, [PROV:] provenance, the single-source-of-truth map, generated perf blocks, which numbers may appear in prose, plan-doc closure. Triggers on "which doc does this go in", "docs lint failed", "add a doc", "this claim is stale", "update the README", "PROV block", "layer", "STALE.md". Do NOT use for the CHANGELOG or a release body (shipping-prs), or for code-comment accuracy (codebase-audit).

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。

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".

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Phase 4 of 5 — implements tasks from TASKS-<N>-<slug>.md (with ARCH-<N>-<slug>.md as architecture-only context) using mode-appropriate verification (tdd, test-after, ui, checklist); pass 'auto' to run without stepping. Use only when the user asks to run Phase 4 or implement tasks from an ARCH doc — never trigger automatically from a coding request.

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Executes a QA specification (specs/qa/QA-<N>-<slug>.md) written by /plan-qa: drives every step with the plan's drivers, verifies each Expected line mechanically ([assert]) or by evidence-backed judgment against the plan's written criterion ([judge]), pauses at named operator handoffs, and writes the results artifact QA-RESULTS-<N>-<slug>.md. Use only when the user asks to run or execute a QA plan — never trigger automatically.

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。

Parse all `# implements:` annotations in source code and emit `library/_code-index.md` as a shelf-index. Also extracts cross-module import edges and writes `library/_dependency-edges.md`. Idempotent — re-running produces byte-identical output if no annotations or imports changed.

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Build or update a CONTEXT.md domain glossary for the current project. Establishes shared vocabulary with term definitions, avoid-aliases, and relationships so agents use consistent, concise language. This skill should be used when starting a new project, when domain terminology is inconsistent across sessions, when the user says "build a glossary", "define our terms", "shared vocabulary", or "domain language", or when an existing CONTEXT.md needs updating after new domain concepts emerge.

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Audits Claude Code repositories. Two-phase workflow — Phase 1 sweeps 14 hygiene categories (permissions, hooks, CLAUDE.md bloat, Skills hygiene, process-abstraction, plus structural) and an optional 7-layer leak check. Phase 2 executes [APPROVED] markers from the report file. Use for repo cleanup, pre-distribution sanity, post-feature hygiene, environment audit, process-abstraction discovery. Trigger on "audit my CC repo," "sweep my repository for hygiene," "check this CC environment," "find process abstraction candidates," "review my CLAUDE.md," "clean up my .claude directory," "pre-distribution hygiene check," "find layer leaks in my CC setup." Routes structural findings to rootnode-cc-design REMEDIATE. Composes with rootnode-critic-gate. CC-only — does not scan Claude Projects. Do NOT use for building a Skill (use rootnode-skill-builder), compiling a prompt (use rootnode-prompt-compilation), scoring a single prompt (use rootnode-prompt-validation), or auditing a Project (use rootnode-project-audit).

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Builds, validates, packages, tests, and optimizes deployment-ready Skill files (SKILL.md + references/ + scripts/ + agents/) from design specifications. Three pre-build gates + 9-dimension quality gate (with D9a/b/c empirical evidence tiers) + behavioral iteration loop + automated description optimizer + blind version comparator. Use for building, reviewing, revising, testing, or optimizing Skills. Trigger on: "build this Skill," "review this Skill," "test my Skill," "optimize the description," "compare these Skill versions," "should this be a hook instead," "build a template first." Also: "my Skill doesn't activate," "my description truncates," "my SKILL.md is too long," "my Skill is overtriggering." Do NOT use when designing Skill methodology — that's design work, not packaging. Do NOT use for prompt compilation, project auditing, or prompt scoring (use rootnode-prompt-compilation, rootnode-project-audit, or rootnode-prompt-validation if available).

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。

Designs Claude Code prompts and CC environments. Produces CLAUDE.md drafts, agent topologies, scope-authorization frameworks, halt triggers, Skills/hooks/MCP plans, chat-to-CC handoff specs, and EXECUTION_PLAN.md remediation plans. Five modes: DESIGN (new CC deployments), EVOLVE (updates from friction), RESEARCH (evaluate a CC tool/pattern), TEMPLATE (reusable artifacts), REMEDIATE (consume hygiene findings → produce + execute plan). Do NOT use REMEDIATE for direct cleanup (Cat 1–10 — use rootnode-repo-hygiene Phase 2). Do NOT use for hygiene scanning (rootnode-repo-hygiene), chat prompts (rootnode-prompt-validation), or chat Projects (rootnode-project-audit).

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。

This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown content.

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~/.claude/skills/ や任意のパスにあるスキルを claude-code-plugin リポジトリの 正しいディレクトリ構造 (<category>/<name>/skills/<name>/SKILL.md) にコピー・配置するスキル。 スキルの構造検証、既存スキルとの重複チェック、配置後の確認も行う。 Use when: スキルをプラグインリポジトリに公開したい、スキルを移動したい、 スキルをパッケージ化したい、スキルを整理したい。 Triggers: "スキルを公開", "プラグインに追加", "skill publish", "スキルを移動", "claude-code-plugin に追加", "スキルをコピー", "パッケージ化"

プロダクトの基盤情報（課題仮説・ソリューション・市場スコープ・原体験・ビジネスモデル・競合・差別化）を ユーザーとの対話で掘り下げ、構造化されたドキュメント（product-context.md）として整理するスキル。 「自分のプロダクトって結局何なの？」を一緒に整理する壁打ち相手として機能する。 ペルソナ作成、LP設計、機能優先順位、マーケティング戦略など、 プロダクトの方向性に関わるあらゆる意思決定の「前提情報」を1ファイルにまとめる。 コードベースがあれば自動で読み取れる部分は埋め、残りをユーザーにヒアリングする。 Use when: 「プロダクトを整理したい」「ターゲット整理」「コンテキスト作成」 「ペルソナの前に」「誰向けのプロダクトか整理」「プロダクト情報まとめ」 「product context」「プロダクト定義」「プロダクトの前提」 「product discovery」「誰向け」と言われた時。 新しいプロジェクトの立ち上げ時や、方向性を見直したい時にも使う。 ペルソナ作成スキルを使う前に、まずこのスキルでプロダクトの前提を整理すべき。 Triggers: "product discovery", "product context", "プロダクト整理", "ターゲット整理", "コンテキスト作成", "プロダクト定義", "プロダクトの前提", "ペルソナの前に", "誰向け", "プロダクト情報まとめ", "プロダクトって何", "何を作ってるか整理", "どんな需要があるか"

App Store / Google Play のメタデータ最適化（ASO）を、4つの専門エージェントチームで実行するスキル。 キーワード調査、宣伝テキスト作成、競合分析、多言語ローカライゼーションの4観点から App Store 向けテキスト一式（タイトル、サブタイトル、キーワード、説明文、宣伝テキスト、What's New）を 生成し、最終レポート(MD)を出力する。多言語対応（日本語・英語+他言語）。 Use when: App Store の説明文を書きたい、ASO対策したい、キーワード選定したい、 宣伝文章を作りたい、App Store テキストを多言語化したい、What's New を書きたい。 Triggers: "ASO", "App Store", "宣伝文", "説明文", "キーワード", "サブタイトル", "What's New", "promotional text", "app description", "keyword optimization", "ストア掲載", "メタデータ", "アプリ紹介文", "Google Play", "多言語ASO"