Local LLM Config

Local LLM Config Skill

When to use

Use when you need to configure, switch, tune, or benchmark locally-hosted LLMs (Ollama, LMStudio, llama.cpp, etc.) for any skill-enabled agent.

Essential for:

• Setting up development environments with local LLMs to avoid API costs
• Ensuring privacy by keeping all LLM processing on the local machine
• Optimizing model selection based on available hardware resources
• Switching between different models for various tasks (reasoning vs. speed)
• Troubleshooting LLM connectivity and performance issues
• Creating reproducible LLM configurations for team sharing

What it does

• 🔍 Detection: Automatically detects installed LLM backends via common CLI tools (ollama, lmstudio, llama-cli) or environment variables
• 📋 Listing: Lists available models with detailed specifications including size, quantization, and estimated memory requirements
• ⚙️ Configuration: Generates model-selection configuration files (YAML format) that agents can consume
• 📊 Benchmarking: Measures model performance including latency, token throughput, and resource utilization
• 🔧 Tuning: Provides helper snippets to generate prompt templates optimized for selected models
• 💾 Persistence: Stores configuration in the agent’s skill directory or profile for consumption by other skills
• 🔄 Management: Enables switching between models and backends without disrupting ongoing agent operations

Typical workflow

1. Detect: Run local-llm-config detect to see what LLM backends are available and what models they offer
2. List: Use local-llm-config list --verbose to see detailed model specifications for informed selection
3. Select: Choose and configure a model with local-llm-config select --model [--backend ]
4. Benchmark (optional): Verify performance with local-llm-config bench --model [--timeout ]
5. Reference: Point your agent’s settings or downstream skills to the generated configuration file
6. Manage: Switch models later using the select command or view current config with show

Configuration Options

• $LOCAL_LLM_CONFIG_FILE: Path to custom configuration file (default: llm.yaml in skill directory)
• $LOCAL_LLM_CACHE_DIR: Directory for caching model information and benchmark results
• $LOCAL_LLM_VERBOSE: Enable verbose logging (equivalent to --verbose flag)

Supported Backends

• Ollama: ollama command-line interface
• LMStudio: Local model serving via LMStudio application
• llama.cpp: Direct execution via llama-cli or similar binaries
• Auto: Automatic detection of any available backend (default)

References

• https://ollama.com - Ollama LLM framework
• https://lmstudio.ai - LMStudio local LLM interface
• https://github.com/ggerganov/llama.cpp - llama.cpp efficient LLM inference
• Hugging Face model repositories for compatible model formats
• LiteLLM and LlamaIndex integration patterns for LLM configuration

Triggers

This skill activates when the user mentions:

• "configure local LLM"
• "set up ollama"
• "choose local model"
• "benchmark LLM performance"
• "switch llm backend"
• "detect available models"
• "local llm configuration"
• "manage llama.cpp"
• "lmstudio setup"
• "optimize model selection"