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Pydantic Ai

Listed
itechmeat·vlatest·backend·MIT

Generates Pydantic models from natural language descriptions, streamlining data validation and API development.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add itechmeat/llm-code --skill pydantic-ai -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

itechmeat

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: agent-server.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    agent-server.comundeclared

    What it found

    • skills/pydantic-ai/references/integrations.md:248contacts agent-server.com
      client = A2AClient('http://agent-server.com')

  • No credential access: failed

    Touches credential material in 1 place.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

    What it found

    • skills/pydantic-ai/references/embeddings.md:100AWS credentials file
      # Uses AWS credentials from environment or ~/.aws/credentials

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned itechmeat/llm-code at 7ae8a00524.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7ae8a00524
Manifest
skills/pydantic-ai/SKILL.md
Scanned
10 files, 4,592 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to leverage Pydantic, a Python library for data validation and parsing. It allows agents to define schemas for structured data, ensuring that incoming or outgoing information conforms to specific formats. This is particularly useful when interacting with APIs or processing user input where consistent data structures are required.

When to use it

  • API Integration: When an agent needs to interact with a REST API that uses JSON payloads and requires strict data validation.
  • Data Extraction: To parse structured data from text, such as extracting information from configuration files or logs.
  • User Input Validation: To ensure user-provided input adheres to predefined formats before processing it further.
  • Generating Structured Output: When an agent needs to produce output in a consistent and well-defined format for another application or system.

Key capabilities

  • Data validation using Pydantic schemas
  • Parsing data into Python objects based on defined models
  • Defining custom data types and constraints
  • Automatic generation of JSON Schema from Pydantic models

Example prompts

  • "Create a Pydantic model for an order with fields 'item_name', 'quantity', and 'price'."
  • "Validate this JSON data against the 'Order' schema: { 'item_name': 'widget', 'quantity': 5, 'price': 10.99 }"
  • "Generate a JSON Schema from this Pydantic model."

Tips & gotchas

  • Requires familiarity with Python and Pydantic syntax.
  • The agent's ability to generate correct Pydantic models depends on the clarity of the prompt and desired data structure.