Agent skill · software engineering · jeremylongshore
ollama-setup
Configure auto-configure Ollama when user needs local LLM deployment,
Why this skill is useful
Adds executable scripts for auto-configuring Ollama for local LLM deployment, including hardware assessment and model selection.
What it needs
Requires brew, curl, docker installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Ollama Setup Overview Auto-configure Ollama for local LLM deployment, eliminating hosted API costs and enabling offline AI inference. This skill handles system assessment, model selection based on available hardware (RAM, GPU), installation across macOS/Linux/Docker, and integration with Python, Node.js, and REST API clients. Prerequisites macOS 12+, Linux (Ubuntu 20.04+, Fedora 36+), or Docker runtime Minimum 8 GB RAM for 7B parameter models; 16 GB for 13B models; 32 GB+ for 70B models Optional: NVIDIA GPU with CUDA drivers for accelerated inference (nvidia-smi to verify) Optional: Apple Silicon (M1/M2/M3) for Metal-accelerated inference on macOS Disk space: 4-40 GB depending on model size (quantized weights) Package manager: brew (macOS), curl (Linux), or docker (containerized) Instructions 1. Detect the host operating system and available hardware using uname -s, free -h (Linux) or vmstat (macOS), and nvidia-smi (if GPU present) 2. Select appropriate models based on available RAM: 8 GB: llama3.2:7b (4 GB), mistral:7b (4 GB), phi3:14b (8 GB) 16 GB: codellama:13b (7 GB), mixtral:8x7b (26 GB quantized) 32 GB+: llama3.2:70b (40 GB), codellama:34b (20 GB) 3. Install Ollama using the platform-appropriate method: macOS: brew install ollama && brew services start ollama Linux: curl -fsSL https://ollama.com/install.sh sh && sudo systemctl start ollama Docker: docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama 4. Pull the recommended model: ollama pull llama3.2 5. Verify the installation by listing available models (ollama list) and running a test prompt (ollama run llama3.2 "Say hello") 6. Confirm the REST API is accessible: curl http://localhost:11434/api/tags 7. Configure integration with the target application using the appropriate client library (Python ollama, Node.js ollama, or raw HTTP) 8. Set up GPU acceleration if NVIDIA or Apple Silicon hardware is detected 9. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/ollama-setup