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概要と使いどころ
Scan and redact PII and sensitive data (emails, phone numbers, SSNs, API keys, IP addresses, credentials, amounts, company/person names) from repository files. Includes a pre-commit hook to block commits containing PII. Use when asked to audit code for sensitive data, sanitize files before publishing, or install PII detection hooks. For application security hardening see security-engineering.
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。
Use when reviewing or auditing a library of Claude Code skills, agents, slash commands, and workflows — frontmatter correctness, routing quality, tool allowlists, command arg-hints, workflow meta/phase coherence, cross-reference coherence, single-responsibility, file structure, and anti-pattern detection. Triggers on mentions of "review skills", "audit agents", "skill library", "agent definition review", "review this command", "review this workflow", "is this skill right", or when iterating on `.claude/skills/`, `.claude/agents/`, `.claude/commands/`, or `.claude/workflows/` directories. For code review of source code see code-review-and-quality.
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。
Use when implementing features, fixing bugs, or refactoring code that may invalidate existing docs. Detects stale documentation by matching the code diff against in-repo doc files and applies targeted updates. Relevant when code changes rename, remove, or add APIs, fields, config keys, or CLI flags. Also applies when the user says "update docs", "check docs", or "are the docs stale", or when reviewing a branch for documentation accuracy.
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。
Use when starting work in a repository under repositories/ that may lack CI configuration. Detects missing CI workflows (GitHub Actions, GitLab CI, CircleCI) and alerts the user to add one. Skips repos marked as research-only.
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 for hypothesis testing, literature gap analysis, experimental validation, and data-driven discovery. Enforces adversarial review (Reviewer 2), 32 quality gates, tree search over hypotheses, confounder harness for quantitative claims, and serendipity detection. TRIGGER when: user asks to analyze scientific data, test hypotheses, validate findings, search for research gaps, design experiments, or investigate results. DO NOT TRIGGER when: pure code review, documentation writing, devops tasks, general conversation, or non-scientific data transformation.
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 adversarial review, tree search, and serendipity detection. Use when: exploring hypotheses, validating findings against literature, running computational experiments with quality gates, or hunting for unexpected discoveries. Do NOT use for simple Q&A, code editing, or non-research tasks.
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。
Intelligently organize files across your computer by understanding context, detecting duplicates, suggesting folder structures, and automating cleanup. Maintain a clean digital workspace without manual effort.
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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 cryptographic code to detect operations that leak secret data through execution timing variations.
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。
Smart dependency management for any language. Auto-detects project type, applies safe updates automatically, prompts for major versions, diagnoses and fixes dependency issues.
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。
Write unit and integration tests for Angular v20+ applications using Vitest or Jasmine with TestBed and modern testing patterns. Use for testing components with signals, OnPush change detection, services with inject(), and HTTP interactions. Triggers on test creation, testing signal-based components, mocking dependencies, or setting up test infrastructure. Don't use for E2E testing with Cypress or Playwright, or for testing non-Angular JavaScript/TypeScript code.
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。
Create modern Angular standalone components following v20+ best practices. Use for building UI components with signal-based inputs/outputs, OnPush change detection, host bindings, content projection, and lifecycle hooks. Triggers on component creation, refactoring class-based inputs to signals, adding host bindings, or implementing accessible interactive components.
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。
Detect cell-ordering / enchainement problems in Jupyter notebooks (canonical-order slippage, misplaced or forgotten cells). Arguments: [target] [--severity HIGH|MED|LOW] [--json]
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。
Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".
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。
Track data flow between function parameters, calls, and arguments using taint analysis. Use when detecting vulnerabilities like command injection, buffer overflows, or tracing user input to dangerous functions.
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。
Calculate statistical power and sample sizes for RCTs. Use when user mentions: power analysis, sample size calculation, minimum detectable effect, MDE, statistical power, effect size, clustering effects, design effect, ICC, intra-cluster correlation.
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。
Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.
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。
Detects chart patterns in OHLC data from natural-language hypotheses or visual chart selections.
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。
How to fetch the latest github/gh-aw release binary — either as the gh CLI extension (gh aw ...) or as a raw platform binary downloaded from GitHub Releases — including how to resolve the latest tag, pick the right asset, and verify its checksum.
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。
Error Message Style Guide for Validation Errors
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。
Instructions for using the struct tag-based console rendering system in Go
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。
How to manually regenerate the compiled agentic workflow .lock.yml files when the gh-aw Version Check workflow reports version drift. All workflows track the newest github/gh-aw release or prerelease.
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。
Use lane in this repository when the user asks to create or work in an isolated worktree.
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。
Codebase audit + refactoring workflow. Dead code detection, God Object splitting, duplicate function removal, design flaw identification, frontier comparison verification. Triggered by "audit" ("감사"), "dead code" ("데드코드"), "refactor" ("리팩토링"), "god object", "duplication" ("중복"), "design flaw" ("설계 결함") keywords.
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。