Phase MODEL - Génère specs + ADR + rules
Skills（SKILL.md）は、AIエージェント（Claude Code、Cursor、Codexなど）に特定の能力を追加するための設定ファイルです。
詳しく見る →Phase MODEL - Génère specs + ADR + rules
Expert in .faf (Foundational AI-context Format) files for persistent project context. Use when working with .faf files, project DNA, CLAUDE.md bi-sync, faf-cli commands, MCP server configuration, or AI-readiness scoring (0-100%). Updated for v2.8.0 Tool Visibility System.
Accessibility: WCAG compliance, assistive technologies, inclusive design.
AI agents: autonomous agents, multi-agent systems, LangChain, LlamaIndex, MCP.
API development: REST, GraphQL, OpenAPI, versioning, auth, rate limiting.
Communication: stakeholder dialogue, Mom Test, conflict resolution, feedback, storytelling.
DevOps orchestrator: infrastructure and CI/CD.
SDD feature executor: sequential task execution with quality gates, test validation.
Frontend: Tailwind, CSS-in-JS, design tokens, UI libraries, PWA, accessibility.
Growth marketing: experiments, analytics, A/B testing, AARRR metrics, retention.
Marketing orchestrator: GTM, content, growth, conversion optimization.
ML/AI orchestrator: LLM integration, RAG, ML Ops, agents, multimodal.
Agile PM specialist: Scrum, Kanban, SAFe ceremonies, PM tools (Jira, Linear, ClickUp, GitHub, Azure DevOps), dashboards, team development, AI in PM, hybrid delivery. 28 methodologies.
PM orchestrator: coordinates agile and traditional PM approaches.
RAG engineering: embeddings, chunking, vector databases, hybrid search, reranking.
Research: idea generation, market research, competitors, personas, pricing, validation.
SDD planning: specifications, design docs, implementation plans, task creation.
SDD workflow: specs, designs, implementation plans, quality gates.
UI design: wireframes, prototypes, design systems, visual design.
User research: personas, user interviews, jobs-to-be-done, pain point research.
UX research: user interviews, usability testing, personas, journey maps.
UX/UI orchestrator: user research, UI design, accessibility.
**Use this skill when**: Writing tests, fixing bugs, adding features, or modifying gateway layers in Python projects.
Complete fal.ai text-to-video system. PROACTIVELY activate for: (1) Kling 2.0/2.5/2.6 Pro video generation, (2) Sora 2 for creative videos, (3) LTX Video with audio, (4) Runway Gen-3 Turbo for fast iteration, (5) Luma Dream Machine, (6) Video duration and aspect ratio, (7) Motion prompt engineering, (8) Camera movement keywords. Provides: Model endpoints, quality tiers, prompt structure, duration options. Ensures cinematic video generation with proper motion description.
Build FastAPI applications using Clean Architecture principles with proper layer separation (Domain, Infrastructure, API), dependency injection, repository pattern, and comprehensive testing. Use this skill when designing or implementing Python backend services that require maintainability, testability, and scalability.
Master FastAPI dependency injection for building modular, testable APIs.
Configure multi-platform deployment for FastAPI applications including Docker containerization, Railway, DigitalOcean App Platform, and AWS deployment. Use when deploying FastAPI apps, setting up production environments, containerizing applications, configuring cloud platforms, implementing health checks, managing environment variables, setting up reverse proxies, or when user mentions Docker, Railway, DigitalOcean, AWS, deployment configuration, production setup, or container orchestration.
Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke for async SQLAlchemy, JWT authentication, WebSockets, OpenAPI documentation.
Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.
Comprehensive guide for building production-ready microservices with FastAPI including REST API patterns, async operations, dependency injection, and deployment strategies
FastAPI_Pytest_TDDHelper
FastAPI framework workflow guidelines. Activate when working with FastAPI projects, uvicorn, or FastAPI-specific patterns.
fastapi
>
Build production-ready MCP servers using FastMCP framework with proven patterns for tools, resources, prompts, OAuth authentication, and comprehensive testing. Use this when creating FastMCP-based MCP servers with features like Google OAuth, multiple resource types, testing with FastMCP Client, or complex tool implementations.
FastMCP Cloud deployment validation, testing, and verification patterns. Use when deploying MCP servers, validating deployments, testing server configurations, checking environment variables, verifying deployment health, tracking deployments, or when user mentions FastMCP Cloud, deployment validation, pre-deployment checks, post-deployment verification, deployment troubleshooting, or deployment lifecycle management.
Build Model Context Protocol (MCP) servers - comprehensive coverage of generic MCP protocol AND FastMCP framework specialization. Use when creating any MCP server (Python FastMCP preferred, TypeScript/Node also covered). Includes agent-centric design principles, evaluation creation, Pydantic/Zod validation, async patterns, STDIO/HTTP/SSE transports, FastMCP Cloud deployment, .mcpb packaging, security patterns, and mid-2025+ community practices. Standalone skill with no external dependencies.
fastmcp
Fast and user-friendly file system search using fd as a replacement for `find` command
Orchestrator skill for the complete feature development lifecycle. Coordinates 5 phases - task selection, component design, build loop, analytics setup, and commit/documentation. Use when building any new feature or enhancement that requires multiple steps.
Framework for making feature prioritization decisions
Complete feature development lifecycle from research to deployment. Uses Gemini Search for best practices, architecture design, Codex prototyping, comprehensive testing, and documentation generation. Full 12-stage workflow.
feature-engineer
Discovers KrakenD features, checks edition availability (CE vs EE), and provides implementation examples
Expand epics into features for an idea (writes to ideas/<IDEA_ID>/runs and updates ideas/<IDEA_ID>/latest)
Manage features.yml for tracking requirements and progress; use proactively ONLY when features.yml already exists, or invoke manually to create one; complements TodoWrite for persistent project state.
Adds feature flag support using LaunchDarkly or JSON-based configuration to toggle features in UI components and Server Actions. This skill should be used when implementing feature flags, feature toggles, progressive rollouts, A/B testing, or gating functionality behind configuration. Use for feature flags, feature toggles, LaunchDarkly integration, progressive rollout, canary releases, or conditional features.
Guides a user through DDD → BDD → TDD → Git for a single feature, staying code-agnostic and interactive.
Use when defining new features, gathering requirements, or writing specifications. Invoke for feature definition, requirements gathering, user stories, EARS format specs.
Comprehensive checklist for implementing new features in Ishkul. Ensures all aspects are covered including frontend, backend, tests, E2E tests, infrastructure, and documentation. Use when starting work on a new feature or enhancement.