Pydantic
CheckedPydantic validates Python data structures against schemas, ensuring data integrity and simplifying API development.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add manutej/luxor-claude-marketplace --skill pydantic -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
manutej
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.
Automated safety checks
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 manutej/luxor-claude-marketplace at 1861cc9720.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 1861cc9720
- Scanned
- 3 files, 3,758 lines
About this skill
What it does
This skill enables AI agents to leverage Pydantic, a modern data validation library, ensuring that input and output data strictly adheres to defined schemas. It facilitates robust type checking and automatic model generation within agent workflows.
When to use it
- Validating complex nested data structures before processing them in an agent pipeline.
- Enforcing strict JSON schema compliance for API responses generated by the agent.
- Automatically generating Python classes from JSON definitions to reduce boilerplate code.
- Catching data errors early during development or production runs to prevent downstream failures.
Key capabilities
- Data Validation: Automatically checks incoming data against defined models.
- Schema Generation: Creates Pydantic models directly from JSON schemas or strings.
- Type Safety: Ensures strict type enforcement for agent inputs and outputs.
- Error Handling: Provides detailed validation error messages for malformed data.
Example prompts
- "Create a Pydantic model for a user profile with fields for name, email, and age."
- "Validate this incoming JSON payload against the defined Order schema using Pydantic."
- "Generate a Python class from this JSON string to handle agent configuration settings."
Tips & gotchas
Ensure your AI agent has access to the pydantic package installed in its environment before attempting validation. Be aware that complex nested models may require recursive configuration for full validation support.