# Build Pydantic AI Agents

Pydantic AI projects need clear patterns for agents, tools, dependencies, and structured outputs. This skill provides concise guidance and examples for building type-safe Python agent applications.

## Install

```bash
npx skillstore add dougtrajano/pydanticai-docs
```

## Metadata

- Status: approved
- Slug: dougtrajano-pydanticai-docs
- Skillstore revision: r1
- Version status: missing
- Tree hash: 868512281d926c6dc3a042503464a95295e278f270b4e0ffe1499bc44b4e890e
- Author: DougTrajano
- GitHub username: DougTrajano
- License: MIT
- Repository: https://github.com/DougTrajano/pydantic-ai-skills/tree/main/examples/skills/pydanticai-docs
- Ref: 72d5025b022c77f7a51bdf5c1637c689c80e89d1
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network, env\_access
- Quality score: 79
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/dougtrajano-pydanticai-docs
- Manifest: https://skillstore.pages.dev/api/skills/dougtrajano-pydanticai-docs/manifest

## Capabilities

- Explains Pydantic AI agents, instructions, tools, output types, and dependencies.
- Shows basic agent creation with synchronous and asynchronous execution patterns.
- Provides examples for tool registration, toolsets, dynamic instructions, and system prompts.
- Covers structured outputs with Pydantic models for validated responses.
- References streaming, retries, test model overrides, and installation commands.

## Use Cases

- Create a First Agent: Learn the minimum structure for a Pydantic AI agent with a provider model and a prompt.
- Add Typed Tooling: Design tools that receive RunContext dependencies and return predictable typed results.
- Validate Agent Output: Use Pydantic models to require structured results that downstream code can parse safely.

## Prompt Templates

### Start a Simple Agent

```
Show me how to create a basic Pydantic AI agent for a short question-answer task.
```

### Add a Tool With Dependencies

```
Help me design a Pydantic AI tool that uses RunContext dependencies for user-specific data.
```

### Return Structured Output

```
Create a Pydantic AI agent pattern that returns a validated Pydantic model for a domain object.
```

### Review a Production Agent

```
Review my Pydantic AI agent design for tools, dependencies, retries, streaming, and test model overrides.
```

## Limitations

- It is a compact reference, not the complete Pydantic AI documentation.
- It may need the online llms.txt document for full API details.
- It does not verify package versions or provider-specific model availability.
- It does not execute or test generated application code.

## Best Practices

- Define dependency and output types before adding tools.
- Keep tool functions narrow, typed, and easy to test.
- Use test model overrides before connecting production providers.

## Anti Patterns

- Leaving outputs untyped when application code expects fixed fields.
- Putting provider credentials directly in examples or prompts.
- Adding broad tools without clear dependency boundaries.

## Security Audit

- Audited at: 2026-07-06T11:44:55.096\+00:00
- Summary: The static findings are false positives caused by Markdown inline code and fenced examples in SKILL.md. The documentation URL points to public Pydantic AI docs, and the api\_key text is only a type field in an example.

## Stats

- Views: 258
- Downloads: 6
- Favorites: 0
- Popularity score: 0