Agent skill · AI & Agents

pydantic-ai

Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill pydantic-ai-sickn33-antigravity-awesome --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 12 KB
Bundled scripts: none
Declared author: suhaibjanjua
Path: skills/ai-llm/pydantic-ai-sickn33-antigravity-awesome/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# PydanticAI — Typed AI Agents in Python ## Overview PydanticAI is a Python agent framework from the Pydantic team that brings the same type-safety and validation guarantees as Pydantic to LLM-based applications. It supports structured outputs (validated with Pydantic models), dependency injection for testability, streamed responses, multi-turn conversations, and tool use — across OpenAI, Anthropic, Google Gemini, Groq, Mistral, and Ollama. Use this skill when building production AI agents, chatbots, or LLM pipelines where correctness and testability matter. ## When to Use This Skill - Use when building Python AI agents that call tools and return structured data - Use when you need validated, typed LLM outputs (not raw strings) - Use when you want to write unit tests for agent logic without hitting a real LLM - Use when switching between LLM providers without rewriting agent code - Use when the user asks about `Agent`, `@agent.tool`, `RunContext`, `ModelRetry`, or `result_type` ## How It Works ### Step 1: Installation ```bash pip install pydantic-ai # Install extras for specific providers pip install 'pydantic-ai[openai]' # OpenAI / Azure OpenAI pip install 'pydantic-ai[anthropic]'

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. How It Works
  4. Step 1: Installation
  5. Step 2: A Minimal Agent
  6. Step 3: Structured Output with Pydantic Models
  7. Step 4: Tool Use
  8. Step 5: Dependency Injection
  9. Step 6: Testing with TestModel
  10. Step 7: Streaming Responses
  11. Step 8: Multi-Turn Conversations
  12. Examples
  13. Example 1: Code Review Agent
  14. Example 2: Agent with Retry Logic
Ships with 1 file
  • metadata.json
Commands it runs
pip install pydantic-ai
Install extras for specific providers
pip install 'pydantic-ai[openai]'       # OpenAI / Azure OpenAI
pip install 'pydantic-ai[anthropic]'    # Anthropic Claude
pip install 'pydantic-ai[gemini]'       # Google Gemini
pip install 'pydantic-ai[groq]'         # Groq
pip install 'pydantic-ai[vertexai]'     # Google Vertex AI
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About this skill
What does the pydantic-ai skill do?

Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill pydantic-ai-sickn33-antigravity-awesome --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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