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Agent Builder Pydantic Ai

Checked
fernandofuc·vlatest·backend

Generates Pydantic schemas and Python code for structured data interactions within AI agents, streamlining 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 fernandofuc/nextjs-c-s --skill agent-builder-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

fernandofuc

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 credential access: passed

    Reads no keys, tokens, keychains or credential files.

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

  • 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 fernandofuc/nextjs-claude-setup at 51264cb573.

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

Commit
51264cb573
Manifest
.claude/skills/agent-builder-pydantic-ai/SKILL.md
Scanned
1 file, 226 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

The agent-builder-pydantic-ai skill enables the creation of AI agents with structured data validation using Pydantic. It allows you to define agent schemas, ensuring that inputs and outputs adhere to specific types and constraints. This promotes reliability and reduces errors in agent interactions by enforcing data consistency.

When to use it

  • Building Agents Requiring Structured Data: Use this when your agents need to process or generate data with a defined format (e.g., financial reports, product catalogs).
  • Ensuring Input Validation: When you want to guarantee the agent receives data in the expected structure and types before processing it.
  • Creating Agents for Specific Tasks: Ideal for tasks like generating invoices, summarizing legal documents, or creating structured database entries.

Key capabilities

  • Pydantic schema definition for agents
  • Data validation using Pydantic models
  • Structured data input and output handling
  • Agent configuration via schemas

Example prompts

  • "Create an agent that generates JSON responses conforming to the 'Order' schema."
  • "Define a Pydantic model for a 'Customer' object with fields for name, email, and address."
  • "Build an agent that validates incoming data against the 'Product' schema before creating a new product record."

Tips & gotchas

  • Requires familiarity with Python and Pydantic.
  • Carefully define your Pydantic schemas to accurately represent the expected data structures for optimal results.