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pydanticai-agents

Build typed, provider-agnostic agents with PydanticAI.

Skill by Biomedical OS Team

IndexedMIT★ 36
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/mdbabumiamssm-pydanticai-agents

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Build typed, provider-agnostic agents with PydanticAI. Use when structured I/O, dependency injection, MCP support, and OpenTelemetry-friendly observability matter more than framework hype.

Use cases: agent-building

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/pydanticai-agents

What's inside

Skill name
pydanticai-agents
Description
Build typed, provider-agnostic agents with PydanticAI. Use when structured I/O, dependency injection, MCP support, and OpenTelemetry-friendly observability matter more than framework hype.
License
MIT
Metadata
author: Biomedical OS Team · version: 2026.04 · upstream_repo: https://github.com/pydantic/pydantic-ai
Instructions
26 lines in SKILL.md · view on GitHub

Agent Skills spec issues

  • name: "pydanticai-agents" does not match folder "PydanticAI_Agents"
  • allowed-tools: must be a space-separated string

Files (3)

  • SKILL.md
  • openai.yaml · agents/
  • sources.md · references/

SKILL.md (first lines)

# PydanticAI Agents

Use this skill when you care about typed contracts, validation, and clean Python engineering as much as raw model output.

## Workflow

1. Define the agent's structured inputs, outputs, and dependencies before writing prompts.
2. Choose the provider model through PydanticAI's model layer so the workflow remains portable.
3. Add tools, dependency injection, and structured outputs only where they simplify the system.
4. Instrument the workflow with Logfire or another OTel-compatible backend before shipping.
5. Back the agent with tests and evals, especially when schema correctness matters.

## Guardrails

- Do not bypass typed outputs for convenience on critical workflows.
- Keep tool schemas strict and explicit.
- Prefer MCP integration through documented interfaces rather than hidden adapters.
- Treat observability as mandatory for production agents.

## Output Requirements

- State the output schema strategy.
- State the provider/model path.
- State the observability path and one failure mode to test.

From mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-, MIT.

More from mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Signals

Stars
36
Forks
8
Open issues
0
Last commit (this folder)
Apr 14, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
2026.04
License
MIT
Repository
github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

More from Biomedical OS Team

Cite this page

pydanticai-agents skill. Agent Mart. https://agentmart.dev/skills/mdbabumiamssm-pydanticai-agents (last updated 2026-10-24)