What is the llm-basics Claude Code skill?

It is a specialized capability for Claude Code that provides architectural patterns, code examples, and optimization tips for developers working with Large Language Models.

Can I use this for local development without an internet connection?

Absolutely, it provides specific instructions and best practices for running models locally using vLLM and Ollama.

How does this help with API costs?

By teaching tokenization efficiency and prompt optimization, it helps developers reduce token usage and lower overall API expenditure.

Does this skill support open-source models?

Yes, it includes implementation guides for Hugging Face models like Llama 2 and Mistral, as well as local inference tools like Ollama.

Does it cover production-ready error handling?

Yes, it includes templates for exponential backoff, retry logic, and troubleshooting common issues like rate limits and high latency.

LLM Fundamentals & Architecture

Name: LLM Fundamentals & Architecture
Author: pluginagentmarketplace

bypluginagentmarketplace

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数据科学与机器学习

Simplifies Large Language Model integration by providing expert guidance on transformer architecture, tokenization, and inference optimization.

关于

The llm-basics skill equips developers with a comprehensive toolkit for understanding and implementing Large Language Model (LLM) features within their applications. It provides essential implementation patterns for the OpenAI API and Hugging Face transformers, explains core concepts like multi-head self-attention, and offers practical configurations for local inference using tools like Ollama and vLLM. Whether you are troubleshooting rate limits, fine-tuning generation parameters, or managing context windows, this skill ensures your AI-driven projects are efficient, scalable, and built on sound architectural principles.

主要功能

2 GitHub stars
Robust error handling templates and retry logic for API communication
Local inference setup instructions for Ollama and vLLM environments
Step-by-step implementation guides for OpenAI and Hugging Face libraries
Comprehensive parameter tuning for temperature, top-p, and frequency penalties
Deep-dive technical patterns for Transformer architecture and tokenization

使用场景

Building production-grade chatbots using OpenAI or open-source models
Troubleshooting tokenization issues and managing context window constraints
Optimizing local LLM deployments for higher performance and lower latency

关于

主要功能

2 GitHub stars
Robust error handling templates and retry logic for API communication
Local inference setup instructions for Ollama and vLLM environments
Step-by-step implementation guides for OpenAI and Hugging Face libraries
Comprehensive parameter tuning for temperature, top-p, and frequency penalties
Deep-dive technical patterns for Transformer architecture and tokenization

使用场景

Building production-grade chatbots using OpenAI or open-source models
Troubleshooting tokenization issues and managing context window constraints
Optimizing local LLM deployments for higher performance and lower latency