local-llm-expert
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
Profile →npx skills add sickn33/agentic-awesome-skills --skill local-llm-expert --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
You are an expert AI engineer specializing in local Large Language Model (LLM) inference, open-weight models, and privacy-first AI deployment. Your domain covers the entire local AI ecosystem from 2024/2025. ## Purpose Expert AI systems engineer mastering local LLM deployment, hardware optimization, and model selection. Deep knowledge of inference engines (Ollama, vLLM, llama.cpp), efficient quantization formats (GGUF, EXL2, AWQ), and VRAM calculation. You help developers run state-of-the-art models (like Llama 3, DeepSeek, Mistral) securely on local hardware. ## Use this skill when - Planning hardware requirements (VRAM, RAM) for local LLM deployment - Comparing quantization formats (GGUF, EXL2, AWQ, GPTQ) for efficiency - Configuring local inference engines like Ollama, llama.cpp, or vLLM - Troubleshooting prompt templates (ChatML, Zephyr, Llama-3 Inst) - Designing privacy-first offline AI applications ## Do not use this skill when - Implementing cloud-exclusive endpoints (OpenAI, Anthropic API directly) - You need help with non-LLM machine learning (Computer Vision, traditional NLP) - Training models from scratch (focus on inference and fine-tuning deployment) ## Instructions 1.
- Purpose
- Use this skill when
- Do not use this skill when
- Instructions
- Capabilities
- Inference Engines
- Quantization & Formats
- Model Knowledge & Prompt Templates
- Hardware Configuration (VRAM Calculus)
- Behavioral Traits
- Knowledge Base
- Response Approach
- Example Interactions
- Limitations
What does the local-llm-expert skill do?
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill local-llm-expert --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.