How do I verify my registry changes are correct?

The skill includes validation commands to run pytest on your capabilities registry and token manager, ensuring new entries don't break existing logic.

Which files does this skill specifically manage?

It is designed to maintain 'massgen/backend/capabilities.py' for model features and 'massgen/token_manager/token_manager.py' for pricing and token limits.

Does this skill support automatic pricing updates?

Yes, it provides specific workflows to pull the latest pricing and context window data from the LiteLLM database and OpenRouter API for real-time accuracy.

When should I use the Model Registry Maintainer skill?

Use this skill whenever an AI provider releases a new model, updates their pricing structure, changes context window limits, or when you need to add a custom backend to your application.

AI Model Registry Maintainer

Name: AI Model Registry Maintainer
Author: Microck

byMicrock

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API Development

Maintains and synchronizes model capabilities, pricing, and context windows within the MassGen backend registry.

About

The Model Registry Maintainer skill provides a standardized workflow for keeping local AI model registries up-to-date with the rapidly evolving landscape of LLM providers. It guides developers through updating backend capabilities and token management files, ensuring that applications have accurate data regarding model release dates, pricing structures, and context window limits. By leveraging external databases like LiteLLM and OpenRouter, this skill streamlines the process of adding new models from providers like OpenAI, Anthropic, and Google while maintaining data integrity through built-in validation and documentation scripts.

Key Features

Standardized workflows for adding new frontier models and providers
Pricing and token limit synchronization for over 500 AI models
Comprehensive registry management for model features and backend capabilities
81 GitHub stars
Built-in validation commands for testing registry updates
Automated data sourcing from LiteLLM and OpenRouter APIs

Use Cases

Adjusting token cost estimation logic after a provider announces pricing reductions
Adding custom or internal organizational models to a backend capabilities framework
Updating project registries immediately following a new model release from a major AI provider

About

Key Features

Standardized workflows for adding new frontier models and providers
Pricing and token limit synchronization for over 500 AI models
Comprehensive registry management for model features and backend capabilities
81 GitHub stars
Built-in validation commands for testing registry updates
Automated data sourcing from LiteLLM and OpenRouter APIs

Use Cases

Adjusting token cost estimation logic after a provider announces pricing reductions
Adding custom or internal organizational models to a backend capabilities framework
Updating project registries immediately following a new model release from a major AI provider