Does it support non-standard AI providers?

Yes, while it has optimized paths for OpenAI, Anthropic, Google, and xAI, it provides the framework to add any custom or emerging provider to the registry.

When should I use the Model Registry Maintainer skill?

Use this skill whenever a provider releases a new model, changes their API pricing, updates context limits, or when you need to add a new backend provider to your project.

How does it handle pricing updates?

It follows a specific resolution order: first checking the LiteLLM database for automated updates, then falling back to hardcoded provider pricing or pattern matching if the model is custom.

Which configuration files are modified by this skill?

It primarily targets capabilities.py for model features and release dates, and token_manager.py for pricing, context windows, and token counting logic.

Can this skill help with documentation?

Yes, it includes commands to regenerate documentation tables and build HTML docs to ensure the user-facing model list matches the backend registry.

Model Registry Maintainer

Name: Model Registry Maintainer
Author: Activer007

byActiver007

Data Science & ML

Maintains and updates LLM model registries, pricing structures, and context window limits across backend configuration files.

About

The Model Registry Maintainer skill is a specialized guide designed to keep the MassGen backend current with the latest AI model releases and pricing changes. It provides a structured workflow for updating capabilities and token management logic, ensuring that Claude can accurately navigate the differences between providers like OpenAI, Anthropic, and Google. By synchronizing metadata across specific Python files and leveraging external databases like LiteLLM, this skill prevents configuration drift and ensures cost estimation remains precise.

Key Features

Automated checks against LiteLLM and OpenRouter pricing databases
Automated documentation generation for backend model tables
Validation protocols using pytest to ensure registry integrity
Cross-file synchronization for model capabilities and release dates
0 GitHub stars
Standardized workflow for adding new AI models and providers

Use Cases

Updating backend support immediately after a new model release from a major provider
Adding custom internal models to the registry that aren't available in public databases
Synchronizing internal pricing models when API costs are reduced or context windows are expanded

About

Key Features

Automated checks against LiteLLM and OpenRouter pricing databases
Automated documentation generation for backend model tables
Validation protocols using pytest to ensure registry integrity
Cross-file synchronization for model capabilities and release dates
0 GitHub stars
Standardized workflow for adding new AI models and providers

Use Cases

Updating backend support immediately after a new model release from a major provider
Adding custom internal models to the registry that aren't available in public databases
Synchronizing internal pricing models when API costs are reduced or context windows are expanded