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Found 148 Skills
Build MCP (Model Context Protocol) servers using the official Python SDK. Covers FastMCP high-level API with @mcp.tool(), @mcp.resource(), @mcp.prompt() decorators, FastAPI/Starlette integration, transports (stdio, SSE, streamable-http), and database integration.
Scaffolds new projects with best practices (CI/CD, Tests, Linting) pre-configured. Ensures a "healthy" starting point for Next.js, FastAPI, Node.js, and more.
Automatically generate comprehensive backend API documentation in AGENTS.md format. Use when the user requests to: (1) Document backend API endpoints, (2) Update backend API specifications after code changes, (3) Create or refresh backend/AGENTS.md with complete API documentation including request/response schemas, business rules, and authentication details, (4) Generate API documentation from FastAPI route files
Generate comprehensive AGENTS.md documentation for backend projects with complete API specifications, business rules, data models, and data flows. Use when (1) Creating AGENTS.md from existing CLAUDE.md, (2) Documenting backend API modules with FastAPI routes, (3) Migrating documentation to AGENTS.md/CLAUDE.md symlink structure, (4) Adding complete API interface documentation to existing specs, (5) Creating module-level AGENTS.md for specific features (mcp, teamo_code, file_system, etc.)
Expert guidance for SQLModel - the Python library combining SQLAlchemy and Pydantic for database models. Use when (1) creating database models that work as both SQLAlchemy ORM and Pydantic schemas, (2) building FastAPI apps with database integration, (3) defining model relationships (one-to-many, many-to-many), (4) performing CRUD operations with type safety, (5) setting up async database sessions, (6) integrating with Alembic migrations, (7) handling model inheritance and mixins, or (8) converting between database models and API schemas.
Fastapi Router Creator - Auto-activating skill for Backend Development. Triggers on: fastapi router creator, fastapi router creator Part of the Backend Development skill category.
Master FastAPI dependency injection for building modular, testable APIs. Use when creating reusable dependencies and services.
Implement secure authentication bridge between Better Auth (Next.js frontend) and FastAPI (Python backend) using JWKS JWT token verification. Use this skill when users need to (1) Integrate Better Auth with FastAPI backend, (2) Implement JWT authentication with JWKS verification, (3) Set up user isolation and authorization in FastAPI endpoints, (4) Configure frontend to send authenticated API requests, or (5) Troubleshoot Better Auth + FastAPI authentication issues.
Trigger when the user wants to create a new FastAPI project, add new features, refactor code, or asks about architectural best practices. This skill enforces 2026 clean architecture with SQLModel, Repository Pattern, full async, and production-ready workflow.
Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".
Comprehensive pytest testing guide for FastAPI backends. Covers unit testing, integration testing, async patterns, mocking, fixtures, coverage, and FastAPI-specific testing with TestClient. Use when writing or updating test code for backend services, repositories, or API routes.
Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.