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Found 96 Skills
Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-driven planning to clarify data models, authentication method, pagination strategy, and caching before writing any code.
Create Galaxy REST API endpoints with FastAPI routers, Pydantic schemas, and manager pattern. Use for: new API routes, FastAPI endpoints, REST resources, Pydantic request/response models, lib/galaxy/webapps/galaxy/api routers, lib/galaxy/schema definitions, API controller creation.
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management.
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Audits and enhances API documentation for FastAPI and REST endpoints. Identifies missing descriptions, incomplete response codes, missing examples, and generates enhanced docstrings, Pydantic model examples, and OpenAPI spec improvements. Triggers on: "generate API docs", "document this API", "OpenAPI for", "add examples to", "improve docstrings", "API documentation audit", "FastAPI docs", "document endpoints", "API reference", "swagger docs", "REST API docs", "endpoint documentation", "response documentation". Use this skill when API endpoints need documentation or documentation audit.
Build typed LLM applications with PydanticAI: schema-constrained outputs, tool integration, validation, retries, and deterministic downstream handoffs. Use when users need reliable structured outputs instead of free-form text generation.
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.
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
Integrate with Affinda's document AI API to extract structured data from documents (invoices, resumes, receipts, contracts, and custom types). Covers authentication, client libraries (Python, TypeScript), structured outputs with Pydantic models and TypeScript interfaces, webhooks, upload patterns, and the full documentation map. Use when building integrations that parse, classify, or extract data from documents using Affinda.
Converts JSON data snippets into Python Pydantic data models.
Generate AI-friendly Python CLIs using Click, Pydantic, and uv. Use when user wants to create a new CLI tool that follows best practices for agentic coding environments.