Can I use this with Pydantic-AI?

Yes, this skill provides specific patterns for creating Pydantic-AI agents directly from datum configurations, including automated environment validation.

Does this skill store my API keys in configuration files?

No. Following the DRY philosophy, datums only specify WHICH environment variables are required; the actual secret values are stored securely in your local .env file.

What is a 'datum' in the b00t system?

A datum is a TOML-based configuration file that defines metadata for an AI provider or model, specifying capabilities and requirements without storing sensitive secrets.

How does the Rust integration benefit my Python workflow?

The core configuration logic is written in Rust for performance and consistency, then exposed to Python via PyO3, ensuring a single source of truth across different languages.

Where are the datum files located?

Datums are typically stored in ~/.dotfiles/_b00t_/ using the .ai.toml extension for providers and .ai_model.toml for specific models.

AI Datum Configuration System

Name: AI Datum Configuration System
Author: elasticdotventures

byelasticdotventures

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Data Science & ML

Manages declarative TOML-based configurations for AI models and providers using a DRY approach with Rust and PyO3 bindings.

The datum-system skill streamlines the management of AI model and provider metadata within the b00t ecosystem. By utilizing a declarative TOML structure stored in dotfiles, it separates architectural requirements from sensitive credentials, ensuring that environment variables are validated while actual API keys remain securely in local .env files. It bridges high-performance Rust logic with Python workflows via PyO3, allowing developers to discover models, validate configurations, and instantiate Pydantic-AI agents with minimal boilerplate and maximum consistency across environments.

Key Features

01DRY architecture leveraging Rust core logic via PyO3 bindings

02Dynamic model discovery and capability mapping

03Automated environment variable requirement validation

04Native Pydantic-AI integration for rapid agent initialization

05Declarative TOML configuration for AI models and providers

0612 GitHub stars

Use Cases

01Validating required environment variables before executing complex AI tasks

02Centralizing model metadata and cost tracking across multiple Python projects

03Adding new AI providers like OpenRouter or Groq to a local agentic workflow

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add elasticdotventures/_b00t_ datum-system

For use in Claude.ai and ChatGPT

Download Skill