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From ollama-local-ai
Automates Ollama installation, hardware-based model selection, GPU setup, and client integration (Python/Node.js/REST) for local LLM inference on macOS/Linux/Docker.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin ollama-local-aiHow this skill is triggered — by the user, by Claude, or both
Slash command
/ollama-local-ai:ollama-setupThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
Optimizes local LLM inference, model selection, VRAM usage, and deployment using Ollama, llama.cpp, vLLM, LM Studio. Covers GGUF/EXL2 quantization and privacy-first setups for offline AI apps.
Deploys and integrates local LLMs with Ollama, LocalAI, and Home Assistant for privacy-focused voice assistants and automation. Covers Ubuntu/Docker installs, Python API clients, and HA config.
Deploys vLLM server on detected hardware (CUDA/ROCm/TPU/CPU), installs in virtual env with uv/pip, starts LLM serving, tests OpenAI-compatible API.
Share bugs, ideas, or general feedback.
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.
nvidia-smi to verify)brew (macOS), curl (Linux), or docker (containerized)uname -s, free -h (Linux) or vm_stat (macOS), and nvidia-smi (if GPU present)brew install ollama && brew services start ollamacurl -fsSL https://ollama.com/install.sh | sh && sudo systemctl start ollamadocker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollamaollama pull llama3.2ollama list) and running a test prompt (ollama run llama3.2 "Say hello")curl http://localhost:11434/api/tagsollama, Node.js ollama, or raw HTTP)See ${CLAUDE_SKILL_DIR}/references/skill-workflow.md for the detailed workflow with code snippets.
http://localhost:11434| Error | Cause | Solution |
|---|---|---|
ollama: command not found | Installation incomplete or PATH not updated | Re-run install script; restart shell session; verify /usr/local/bin/ollama exists |
| Model pull fails with timeout | Network connectivity issue or Ollama registry unreachable | Check internet connection; retry with ollama pull --insecure behind corporate proxy |
| Out of memory during inference | Model size exceeds available RAM | Switch to a smaller quantized model (e.g., 7B instead of 13B); close memory-intensive applications |
| GPU not detected | CUDA drivers missing or incompatible version | Install CUDA toolkit >= 11.8; verify with nvidia-smi; restart Ollama service after driver install |
| Port 11434 already in use | Another service occupying the default Ollama port | Stop conflicting service; or set OLLAMA_HOST=0.0.0.0:11435 environment variable |
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Scenario 1: Developer Workstation Setup -- Install Ollama on a macOS M2 machine with 16 GB RAM. Pull codellama:13b for code generation tasks. Integrate with a Python FastAPI application using the ollama Python package. Expected throughput: 30-50 tokens/second on Apple Silicon.
Scenario 2: Air-Gapped Server Deployment -- Install Ollama on an offline Ubuntu server via pre-downloaded binary. Transfer model weights via USB. Configure as a systemd service with auto-restart. Serve llama3.2:7b via REST API for internal team use.
Scenario 3: Docker-Based CI Pipeline -- Run Ollama in a Docker container as part of a CI/CD pipeline for automated code review. Pull mistral:7b, expose the API on port 11434, and integrate with a Node.js test harness that sends code diffs for analysis.