
This gives Claude direct access to the Pydantic AI framework docs, covering agent setup, structured outputs, function tools, dependency injection, and multi-provider configuration. Reach for it whenever you're building AI agents in Python and need to wire up tools, validate LLM responses with Pydantic models, or configure streaming and context. The skill loads core concepts inline and routes to specific reference files for deeper topics like message history, toolsets, and MCP integration. It's opinionated about not fabricating API details, which keeps answers grounded in the actual framework. If you're prototyping agents or debugging tool calls, this saves you from tab-switching between docs and your editor.
$ npx -y skills add dougtrajano/pydantic-ai-skills --skill pydanticai-docs --agent claude-codeInstalls into .claude/skills of the current project.
Pydantic AI is a production-grade Python agent framework for building type-safe, dependency-injected Generative AI applications. It supports multiple LLM providers, structured outputs via Pydantic models, and composable multi-agent patterns.
from pydantic_ai import Agent
agent = Agent(
'openai:gpt-4o', # model string: provider:model-name
system_prompt='Be helpful.',
)
result = agent.run_sync('What is the capital of France?')
print(result.output)
For full constructor parameters, run methods, and streaming: load references/AGENT.md.
@agent.tool)from pydantic_ai import Agent, RunContext
agent = Agent('openai:gpt-4o', deps_type=str)
@agent.tool
def get_user_name(ctx: RunContext[str]) -> str:
"""Return the current user's name."""
return ctx.deps
result = agent.run_sync('What is my name?', deps='Alice')
Use @agent.tool_plain when you don't need RunContext. For tool registration, return types, and retries: load references/FUNCTION_TOOLS.md.
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