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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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 creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
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 working on multiple branches simultaneously, context switching without stashing, reviewing PRs while developing, testing in isolation, or comparing implementations across branches - provides git worktree commands and workflow patterns for parallel development with multiple working directories.
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 and configure git hooks with intelligent project analysis, suggestions, and automated testing
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。
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement 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。
Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
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Use before writing any type of tests. Distills 14 industry sources into deterministic decision gates, schemas, and worked test examples.
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。
Run GDScript test suites from the command line with `godot --headless`, using a SceneTree/MainLoop runner script that exits non-zero on failure so CI can gate merges. Use when a Godot project needs unit tests without opening the editor, when wiring a CI job (GitHub Actions or similar) that must fail the build on a failing `.gd` test, or when a `godot --headless` invocation hangs, opens a window, or exits 0 despite failed assertions.
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。
Premise, theme, controlling idea, subject selection and logline for scripts (前提/主题/立意/选材/戏核) — merged from Lajos Egri's premise-as-tyrant and dialectic, Robert McKee's controlling idea (value + cause) and idea/counter-idea, Neill Hicks' five premise questions, Lisa Cron's "third rail" (desire vs misbelief, origin scene, what-if), Julian Hoxter's logline and theme-attitude rule, Blake Snyder's logline/genre/title tests, and Lu Jun's Chinese craft chapters on 选材要严/开掘要深/视角要新/戏核要好/情节要奇. Use when turning a vague idea into a premise, finding what a story is really about, choosing a subject and angle, testing a logline, deciding the ending from the theme, or diagnosing a script that "has events but no story".
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。
From an idea to a sellable series design; series engine / franchise, story pattern, character webs that keep generating conflict, pilot types, and the documents that sell them (剧集引擎/franchise/故事模式/人物网/试播集类型/剧集圣经/跳板/提案). Merged from Rabkin's four-element franchise and story landmines, Landau's tacit contract and pitch recipe, Oberg's series types and bible specs, Douglas's franchise-plus-springboard and bible tiers, Goldberg & Rabkin's 100-episode test, Blum's concept/presentation/bible ladder, Miller's ten-component treatment, Calvisi's Compelling Crisis, Smith's series format, plus engines read off Sopranos, Fleabag and Succession. Use when turning an idea into a series, testing whether a premise has legs, choosing premise vs typical-episode pilot, designing a sustainable character web, writing a logline, pitch document, bible or treatment, picking a series type, adapting IP into a series, or diagnosing a project that feels "more like a movie than a series".
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。
Fix GitHub issues end-to-end — analysis, branch creation, implementation, testing, and PR submission. Use whenever the user mentions fixing a GitHub issue, says "fix issue
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。
AI DevKit · Orchestrator for structured SDLC phase skills. Use when the user wants to run the full lifecycle or choose the next phase across requirements, design, planning, implementation, testing, and review.
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Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".
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。
Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are 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。
Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.
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。
Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.
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。
DataOps and DS/MLOps testing reference for data tiering, Bronze-to-Silver validation placement, pipeline invariants, pytest categories, and validation-versus-drift. Use when designing, reviewing, or generating data pipelines, transformation code, data validation, or data-science test suites.
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。
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
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 diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
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。
Plan tests for an API endpoint or service — functional, negative, and contract. Use when asked to test an API, write API test cases, plan REST/GraphQL endpoint testing, or validate an API contract. Produces an API test plan — per-endpoint cases (status codes, schema, auth, validation, errors), boundary/negative cases, contract checks, and non-functional notes — so the API is verified beyond the happy 200.
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Write a specification section that a contractor can price accurately and a site can build from — performance stated, products defined, workmanship and tolerances given, and the coordination with the drawings made explicit. Use when asked to write a specification, spec a material or system, prepare an NBS-style section, or when a specification produced wildly different tender prices. Produces the scope and interface definition, the performance requirements, the product or system definition, execution and tolerance requirements, and the submittals and testing schedule.
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。
EMC pre-compliance risk analysis for KiCad PCB designs — 18 check categories, 44 rule IDs covering ground planes, decoupling, I/O filtering, switching harmonics, clock routing, differential pair skew, board edge radiation, PDN impedance, return paths, crosstalk, ESD protection, shielding, and magnetic leakage from switching inductors. Produces severity-ranked risk report with pre-compliance test plan. Supports FCC Part 15, CISPR 32, CISPR 25 (automotive), MIL-STD-461G. SPICE-enhanced when available. Use when the user asks about EMC, EMI, radiated/conducted emissions, FCC compliance, CE marking, CISPR, ground plane issues, decoupling strategy, clock routing EMC, switching noise, differential pair skew, or whether their board will pass EMC testing. Also for "will this pass FCC?", "check my EMC", "is my ground plane okay?", "check my decoupling", or "generate an EMC test plan".
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。
Help users de-risk new product bets by testing core assumptions, building low-fidelity prototypes, and gathering high-signal evidence before investing heavy engineering resources.
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Interactive workflow partner for creating, testing, and improving AI agent skills with waza. USE FOR: run my evals, check my skill, compare models, create eval suite, debug failing tests, is my skill ready, ship readiness, interpret results, improve score. DO NOT USE FOR: general coding, non-skill work, writing skill content (use skill-authoring), improving frontmatter only (use sensei).
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。