Individual skill
ollama-setup
Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Trigger phrases: "install ollama", "local AI", "free LLM", "self-hosted AI", "replace OpenAI", "no API costs". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
skills/.curated/ollama-setup/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
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- llm Declared skill tag
- devops Derived category
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SKILL.mdcore · 5,724 bytesreferences/errors.mddoc · 319 bytesreferences/examples.mddoc · 14,255 bytesreferences/skill-workflow.mddoc · 1,729 bytes
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# 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)
