pydantic-evals

Test and evaluate AI agents and LLM outputs using code-first evaluation framework with strong typing. Use when the user wants to: (1) Create evaluation datasets with test cases for AI agents, (2) Define evaluators (deterministic, LLM-as-Judge, custom, or span-based), (3) Run evaluations and generate reports, (4) Compare model performance across experiments, (5) Integrate evaluations with Pydantic AI agents, (6) Set up observability with Logfire, (7) Generate test datasets using LLMs, (8) Implement regression testing for AI systems.

Haiku Rag

Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling

pronounce-word — speak the word out loud

**Purpose.** When the user asks how to pronounce an English word — and especially a project, product, or programmer-jargon name (`kubectl`, `nginx`, `Pydantic`, `LaTeX`, `JSON`, ...) — don't just respond in text. Play the audio so they can hear the *community* reading, then add a short text capt

Io.Github.Terry Li Hm/Vivesca

Opinionated MCP server — Pydantic output schemas, 7 domains, built to evolve.

sync-acp-spec

Sync the ACP (Agent Client Protocol) schema implementation with the official reference repo by comparing Rust source types against our Python Pydantic models.

Pydantic AI Documentation Skill

atomic-agents

This skill should be used when the user asks to "create a schema", "define input/output", "add fields", "validate data", "Pydantic schema", "BaseIOSchema", or needs guidance on schema design patterns, field definitions, validators, and type constraints for Atomic Agents applications.

pydantic

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.

dhi-python

Ultra-fast data validation library for Python (520x faster than Pydantic). Use when building validated data models, API request/response schemas, or configuration objects. Provides Pydantic v2-compatible BaseModel API with Zig-powered native validation.

mcp-scaffold

Scaffold production-ready Python MCP servers using FastMCP. Use when creating new MCP servers, initializing MCP projects, generating server boilerplate, or setting up MCP development environments. Supports all MCP primitives (tools, resources, prompts) with Pydantic validation, async patterns, and proper project structure.

dbt-parser-refresh

Refreshes dbt artifact schemas from dbt-labs/dbt-core and regenerates Pydantic parser classes. Use when the user asks to update parsers, sync with upstream, download dbt schemas, or regenerate parser models.

backend-dev

Backend development skill for Resume Matcher. Handles FastAPI endpoints, Pydantic schemas, TinyDB operations, LiteLLM integration, and Python service logic. Use when creating or modifying backend code.

api-docs-generator

Audits and enhances FastAPI and REST API documentation: missing descriptions, response codes, examples, docstrings, Pydantic models, OpenAPI spec. Triggers on: "generate API docs", "document this API", "OpenAPI for", "FastAPI docs", "document endpoints", "swagger docs".