datum-system

Centralize and validate AI model environment variables using TOML datums.

14|1|Updated Apr 22, 2021
One-click install
npx skills add https://github.com/elasticdotventures/_b00t_ --skill datum-system
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: datum-system
Source: https://github.com/elasticdotventures/_b00t_/tree/main/skills/datum-system
Command: npx skills add https://github.com/elasticdotventures/_b00t_ --skill datum-system

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill resolves the challenges of inconsistent, insecure, and duplicated configuration for AI models and providers, leading to errors, compliance risks, and maintenance headaches.

Core Features & Use Cases

  • Declarative AI Configuration: Manage AI models, providers, and services using clear, TOML-based datum files, providing a single source of truth for all AI-related settings.
  • Secure Environment Validation: Specify which environment variables (e.g., API keys) are required by models without storing their actual values, ensuring secure and validated setups.
  • DRY Integration with Rust: Leverages Rust for core logic via PyO3, providing a high-performance, type-safe, and non-duplicated system for configuration parsing and validation.
  • Use Case: Quickly onboard a new AI model like qwen-2.5-72b by creating a simple TOML datum, and the system will automatically validate that all necessary API keys are securely loaded from your .env file, preventing runtime errors.

Quick Start

Help me create a new datum for the 'Groq' AI provider, specifying its models and the GROQ_API_KEY as a required environment variable, ensuring it's ready for use.

Frequently Asked Questions about datum-system

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I centralize and validate environment variables for AI models?

Centralize environment variables using TOML datum files stored in ~/.dotfiles/_b00t_. The system validates required variables like API keys against declared schemas without storing sensitive values, ensuring secure configuration and preventing runtime errors across Rust, Python, and shell workflows.

Can I manage multiple AI providers and models in a single configuration system?

Yes. Create declarative TOML datums for each provider and model, specifying which environment variables each requires. The system automatically discovers available providers and models, validating all dependencies from your .env file in one place.

How do I integrate Rust-based configuration validation with Python workflows?

The Skill uses PyO3 bindings to expose Rust-based parsing and validation logic to Python. This provides type-safe, high-performance configuration handling without duplicating logic across languages, bridging Rust and Python in a single validation pipeline.

What's the best way to onboard a new AI model without duplicating configuration?

Define a new TOML datum specifying the model's name, provider, and required environment variables. The system enforces schema consistency and DRY principles, automatically validating all dependencies and preventing configuration drift.

Does this approach work with environment variables from .env files?

Yes. The system discovers and loads environment values from .env files, validates them against datum schemas, and ensures all required variables are present before runtime, eliminating manual environment setup and validation steps.

What happens if required environment variables are missing or invalid?

The validation system detects missing or invalid environment variables during schema enforcement and raises errors before runtime, preventing silent failures and ensuring compliance with security requirements.