How does this skill get the latest LLM information?

It uses the Context7 MCP to fetch live documentation from official repositories and can perform real-time web searches to capture the most recent pricing and feature updates.

Does it handle specific library versions?

Yes, you can specify versions like 'Next.js 14' or 'React 19' to ensure Claude uses documentation and patterns relevant to your specific tech stack.

Which LLM providers are supported?

It supports all major providers including OpenAI, Anthropic, Google AI, Groq, DeepSeek, Mistral, and Together AI.

Do I need API keys for every provider to use this?

No, the skill primarily fetches public documentation and pricing data. You only need API keys for the specific models you decide to implement in your code.

How can I speed up the documentation lookup?

You can specify library IDs directly, such as 'use library /openai/openai-node', in your prompt to skip the resolution step and retrieve information immediately.

LLM Auto-Update

Name: LLM Auto-Update
Author: johunsang

byjohunsang

0•

데이터 과학 및 ML

Automatically synchronizes the latest LLM model specifications, pricing, and API documentation to ensure optimal architecture decisions.

This skill empowers Claude to maintain a real-time database of Large Language Model (LLM) information, including pricing, context window limits, and new feature releases from providers like OpenAI, Anthropic, and Google. By leveraging the Context7 MCP and automated search capabilities, it ensures that developers are always building with the most current and cost-effective AI models, preventing reliance on outdated knowledge or deprecated APIs during the SaaS development lifecycle.

주요 기능

01Real-time LLM model synchronization via Context7 MCP documentation tools

02Direct library ID resolution for faster documentation fetching and API implementation

030 GitHub stars

04Automated tracking of input/output token pricing across multiple AI providers

05Updates on context window capacities and specialized capabilities like reasoning or vision

06Multi-provider support including OpenAI, Anthropic, Google Gemini, Groq, and DeepSeek

사용 사례

01Optimizing SaaS architecture by comparing the latest model performance against real-time cost data

02Automatically updating API implementation patterns when providers release new SDK versions

03Selecting the most efficient model for specific sub-tasks like high-speed inference or complex reasoning

주요 기능

01Real-time LLM model synchronization via Context7 MCP documentation tools

02Direct library ID resolution for faster documentation fetching and API implementation

030 GitHub stars

04Automated tracking of input/output token pricing across multiple AI providers

05Updates on context window capacities and specialized capabilities like reasoning or vision

06Multi-provider support including OpenAI, Anthropic, Google Gemini, Groq, and DeepSeek

사용 사례

01Optimizing SaaS architecture by comparing the latest model performance against real-time cost data

02Automatically updating API implementation patterns when providers release new SDK versions

03Selecting the most efficient model for specific sub-tasks like high-speed inference or complex reasoning