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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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Run the jigsawR test suite via WSL R execution. Supports full suite, filtered by pattern, or single file. Interprets pass/fail/skip counts and identifies failing tests. Never uses --vanilla flag (renv needs .Rprofile for activation). Use after modifying any R source code, after adding a new puzzle type or feature, before committing changes to verify nothing is broken, or when debugging a specific test failure.

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Optimize Docker build times using layer caching, multi-stage builds, BuildKit features, and dependency-first copy patterns. Applicable to R, Node.js, and Python projects. Use when Docker builds are slow due to repeated package installations, when rebuilds reinstall all dependencies on every code change, when image sizes are unnecessarily large, or when CI/CD pipeline builds are a bottleneck.

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Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.

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Maintain a changelog following Keep a Changelog format. Covers entry categorization (Added, Changed, Deprecated, Removed, Fixed, Security), version section management, and unreleased tracking. Use when starting a new project that needs a changelog, adding entries after completing features or fixes, preparing a release by promoting Unreleased entries to a versioned section, or converting a free-form changelog to Keep a Changelog format.

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Establish and maintain longitudinal baselines of CLI binary contents across versions. Covers marker selection by category (API / identity / config / telemetry / flag / function), weighted scoring, threshold-based system-presence detection, and per-version baseline records. Use when tracking a feature's lifecycle across releases, when probing for dark-launched or removed capabilities, or when verifying that a scanning tool itself still catches known-good markers on old binaries.

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Generate themed Mermaid flowchart diagrams from putior workflow data. Covers theme selection (9 themes including 4 colorblind-safe), output modes (console, file, clipboard, raw), interactive features (clickable nodes, source info), and embedding in README, Quarto, and R Markdown. Use after annotating source files and ready to produce a visual diagram, when regenerating a diagram after workflow changes, or when switching themes or output formats for different audiences.

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Identify and safely gather edible and useful wild plants. Covers safety rules and deadly plant recognition, habitat reading, multi-feature identification methodology, the universal edibility test, sustainable harvesting practices, preparation methods, reaction monitoring, and building knowledge with beginner-friendly universal species. Use when supplementing food supply in a wilderness or survival setting, needing medicinal or utility plants, identifying plants around camp for safety, or in long-term scenarios where foraging extends available rations.

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Field identification of fungi using morphological features, spore prints, habitat analysis, and seasonal context with a safety-first approach. Covers cap, gill, stem, and spore characteristics, look-alike differentiation, toxicity risk assessment, and the critical rule of absolute certainty before consumption. Use when encountering an unknown fungus, foraging for edible mushrooms and needing to confirm species before consumption, assessing whether fungi in a garden or property are harmful, or differentiating an edible species from a dangerous look-alike.

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Establish Service Level Objectives (SLO), Service Level Indicators (SLI), and Service Level Agreements (SLA) with error budget tracking, burn rate alerts, and automated reporting using Prometheus and tools like Sloth or Pyrra. Use when defining reliability targets for customer-facing services, balancing feature velocity against system reliability through error budgets, migrating from arbitrary uptime goals to data-driven metrics, or implementing Site Reliability Engineering practices.

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Create and manage pull requests using GitHub CLI. Covers branch preparation, writing PR titles and descriptions, creating PRs, handling review feedback, and merge/cleanup workflows. Use when proposing changes from a feature or fix branch for review, merging completed work into the main branch, requesting code review from collaborators, or documenting the purpose and scope of a set of changes.

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Read-only audit of a GitHub repository's security posture. Gathers ref protection (rulesets AND classic branch protection), Actions token permissions, code and supply-chain features (Dependabot, secret scanning, push protection, CodeQL), and repo hygiene toggles via `gh api`, then classifies findings against essential / recommended / advanced tiers into a PASS/GAP report. Makes NO changes. Use when reviewing a repo before open-sourcing or a release, auditing a public user-owned repo whose CI auto-commits to the default branch, verifying a hardening change actually took effect, or producing a baseline security posture report for a repository.

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機能検討・プロダクト機能設計の壁打ちスキル。Mode A「壁打ち開始：〇〇機能」で1機能を深掘り、Mode B「アプリの機能を考えたい」でアプリ全体の機能セットを設計。ペルソナ・競合分析・トンマナを統合して議論。

Use when the user needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.

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Parallel SDLC batch runner. Analyzes a list of independent task descriptions, groups them by heuristic file-conflict prediction, dispatches one worktree-isolated background agent per task (each running the full pipeline-orchestrator: BA → Dev → QA → Sec → Docs), and aggregates results into a single batch summary. Use when: - `/sdlc:batch` invokes this skill after parsing its arguments (Step 2 of commands/batch.md) - You have 2+ independent feature/fix descriptions to run through the SDLC pipeline in parallel Do NOT use for: - A single task (use pipeline-orchestrator directly via /sdlc:start) - Tasks that must share a single working tree (this skill isolates each task in its own git worktree)

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FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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React component structure, hooks rules, file naming, project layout, composition patterns, performance idioms, and effects discipline. Apply when implementing or modifying React SPA code. Use this skill to: - Structure a new component or feature folder. - Apply hooks correctly (rules, naming, dependency arrays). - Compose components via children, render props, or compound patterns. - Write effects only when needed (and avoid common misuses). - Pick performance escape hatches (memo, useMemo, useCallback) when justified. Do NOT use this skill for: - State management lib choice (see react-state-management). - Routing primitives (see react-routing). - Form patterns (see react-forms). - Testing (see react-testing).

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Flask web framework conventions: app factory pattern with create_app(), Blueprint registration with url_prefix, MethodView for class-based API views, Marshmallow schema validation, WTForms for HTML forms, Flask-Login for session auth, flask-jwt-extended for stateless API auth, Jinja2 template conventions, error handlers, and extension initialization. Activated automatically by flask-plugin/stack.md. Use this skill to: - Structure Flask applications with the app factory and per-feature Blueprints. - Validate JSON request data with Marshmallow and HTML forms with WTForms. - Implement session-based auth with Flask-Login or token auth with flask-jwt-extended. - Render Jinja2 templates safely or return JSON responses for API mode. - Register global error handlers for consistent error responses. Do NOT use this skill for: - SQLAlchemy ORM model patterns and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Angular Router (built-in `@angular/router`) — route configuration for standalone and NgModule projects, functional guards (Angular 14.1+), lazy loading, route resolvers, typed params via signals/observables, programmatic navigation, route data and meta. Use this skill to: - Configure routes (standalone-style or NgModule-style). - Use functional guards (canActivate as function, preferred over class-based in 17+). - Lazy-load components or feature modules. - Implement auth guards via route meta + functional guards. - Read params/queries via `inject(ActivatedRoute)` + signals or RxJS. Do NOT use this skill for: - General conventions (see angular-conventions). - State management (see angular-state-and-rx). - Forms (see angular-forms). - Testing routes (see angular-testing).

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Modern Java idioms for any JVM project (Java 17+): records, sealed types, text blocks, pattern matching, Optional discipline, streams, immutability, null safety, package layout, var usage, and code organisation. Apply when the project is a Java 17+ project. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write self-documenting, immutable-by-default value types with records. - Model closed type hierarchies with sealed classes and interfaces. - Handle optionality explicitly with Optional instead of returning null. - Write expressive pipelines with Stream API without performance footguns. - Lay out packages consistently (feature-first or layer-first) and keep class responsibilities tight. Do NOT use this skill for: - Framework-specific idioms (Spring annotations, JPA mappings — those live in framework plugin skills). - Build tooling (Maven/Gradle) — see java-foundation:build-tooling. - Testing patterns — see java-foundation:jvm-testing.

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Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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当用户想系统评估一个AI项目是否值得投入时使用，结合概念简单性和数据充足性两个维度。 用户在问"这个想法可行吗"、"我们需要多少数据"、"技术上能做到吗"时激活。 不适用于: 纯数据科学洞察项目、探索性分析、已明确不需要AI的场景。

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E2E manual testing of PRs/branches using docker compose, agent-browser, and API calls. TRIGGER when user asks to manually test a PR, test a feature end-to-end, or run integration tests against a running system.

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。