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Found 85 Skills
Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.
Use when defining or evolving public interfaces, schema boundaries, or pydantic usage in Python. Also use when annotations are missing on public APIs, pydantic models appear everywhere instead of at trust boundaries, contract changes lack migration guidance, or Any/object types are overused across module boundaries.
LangGraph state management patterns. Use when designing workflow state schemas, using TypedDict vs Pydantic, implementing accumulating state with Annotated operators, or managing shared state across nodes.
FastAPI advanced patterns including lifespan, dependencies, middleware, and Pydantic settings. Use when configuring FastAPI lifespan events, creating dependency injection, building Starlette middleware, or managing async Python services with uvicorn.
FastAPI best practices, async patterns, and Pydantic validation
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
Build AI agents with Pydantic AI — tools, capabilities, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, stream output, define agents from YAML, or test agent behavior.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Generate DBeaver config from Pydantic ClickHouse models. TRIGGERS - DBeaver config, ClickHouse connection, database client config.
Python backend implementation patterns for FastAPI applications with SQLAlchemy 2.0, Pydantic v2, and async patterns. Use during the implementation phase when creating or modifying FastAPI endpoints, Pydantic models, SQLAlchemy models, service layers, or repository classes. Covers async session management, dependency injection via Depends(), layered error handling, and Alembic migrations. Does NOT cover testing (use pytest-patterns), deployment (use deployment-pipeline), or FastAPI framework mechanics like middleware and WebSockets (use fastapi-patterns).
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError, (3) Managing async exceptions, (4) Preventing stack trace leakage, (5) Designing custom exception hierarchies.