Perfup
raullenchai/Rapid-MLX
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR
Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs.
Install Ollama Setup
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace ollama-setup --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/ollama-setup .claude/skills/ollama-setup && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ollama-setup" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setup into .claude/skills/ollama-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama-setup", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setupType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace ollama-setup --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.curated/ollama-setup .agents/skills/ollama-setup && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ollama-setup" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setup into .agents/skills/ollama-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama-setup", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace ollama-setup --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.curated/ollama-setup .cursor/skills/ollama-setup && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ollama-setup" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setup into .cursor/skills/ollama-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama-setup", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jeremylongshore/tons-of-skills-marketplace.git --path skills/.curated/ollama-setup--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace ollama-setup --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.curated/ollama-setup .gemini/skills/ollama-setup && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ollama-setup" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setup into .gemini/skills/ollama-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama-setup", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jeremylongshore/tons-of-skills-marketplace ollama-setupInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.curated/ollama-setup .github/skills/ollama-setup && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ollama-setup" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setup into .github/skills/ollama-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama-setup", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace ollama-setup --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.curated/ollama-setup .opencode/skills/ollama-setup && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ollama-setup" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/ollama-setup into .opencode/skills/ollama-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama-setup", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ollama-setup- GitHub stars
- 2.8k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 655 words
- Files
- 4 (incl. references)
- Skills in repo
- 3,342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs.
- Works in 10 steps: Detect the host operating system and… → Select appropriate models based on… → Install Ollama using the… → …
- Needs local LLM deployment
- SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
- Calls ollama, brew and curl; reaches ollama.com
What it does
Ollama Setup is an agent skill from jeremylongshore/tons-of-skills-marketplace. 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.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/errors.md`, `references/examples.md` and `references/skill-workflow.md`). Compatibility notes: Designed for Claude Code
It sits in AI & LLM Engineering, covering LLM inference and serving. It works with Ollama, OpenAI, macOS and Docker. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.
When your agent uses it
- Needs local LLM deployment
- Free AI alternatives
- Wants to eliminate hosted API costs
- Phrases: install ollama
Example prompts
- “install ollama”
- “local AI”
- “free LLM”
- “/ollama-setup”
Requirements
- Python 3
- Node.js
- Docker
- Compatibility (from SKILL.md): Designed for Claude Code
- Pre-approved tools (allowed-tools): Read, Write, Bash(cmd:*)
Workflow steps
10 steps, taken from the first numbered list in SKILL.md.
- Detect the host operating system and available hardware using uname -s, free -h (Linux) or vm_stat (macOS), and nvidia-smi (if GPU present)
- Select appropriate models based on available RAM
- Install Ollama using the platform-appropriate method
- Pull the recommended model: ollama pull llama3.2
- Verify the installation by listing available models (ollama list) and running a test prompt (ollama run llama3.2 "Say hello")
- Confirm the REST API is accessible: curl http://localhost:11434/api/tags
- Configure integration with the target application using the appropriate client library (Python ollama, Node.js ollama, or raw HTTP)
- Set up GPU acceleration if NVIDIA or Apple Silicon hardware is detected
- Configure model persistence and cache directory if non-default storage location is required
- Validate end-to-end inference latency and throughput for the selected model
What it can do on your machine
Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteBash(cmd:*)
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
ollamabrewcurlshdocker
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
ollama.com
Also links to:
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Designed for Claude Code
From compatibility in the SKILL.md frontmatter.
Context cost
Ollama Setup loads about 1.4k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 87 tokens; SKILL.md has 655 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePipes a well-known installer script into a shellSKILL.md:48
- Linux: `curl -fsSL https://ollama.com/install.sh | sh && sudo systemctl start ollama` - NoteRuns commands with sudoSKILL.md:48
SL https://ollama.com/install.sh | sh && sudo systemctl start ollama`
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 655 words, ~1,427 tokens.
Download SKILL.mdSave it as
.claude/skills/ollama-setup/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- ollama-setup
- description
- 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.
- allowed-tools
- Read, Write, Bash(cmd:*)
- compatibility
- Designed for Claude Code
- version
- 1.18.0
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- tags
- ai, deployment, api, llm
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-smito 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), ordocker(containerized)
Instructions
- Detect the host operating system and available hardware using
uname -s,free -h(Linux) orvm_stat(macOS), andnvidia-smi(if GPU present) - 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)
- 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
- macOS:
- Pull the recommended model:
ollama pull llama3.2 - Verify the installation by listing available models (
ollama list) and running a test prompt (ollama run llama3.2 "Say hello") - Confirm the REST API is accessible:
curl http://localhost:11434/api/tags - Configure integration with the target application using the appropriate client library (Python
ollama, Node.jsollama, or raw HTTP) - Set up GPU acceleration if NVIDIA or Apple Silicon hardware is detected
- Configure model persistence and cache directory if non-default storage location is required
- Validate end-to-end inference latency and throughput for the selected model
See ${CLAUDE_SKILL_DIR}/references/skill-workflow.md for the detailed workflow with code snippets.
Output
- Ollama installation confirmed and running as a system service or Docker container
- Selected model(s) pulled and cached locally with verified inference capability
- REST API endpoint accessible at
http://localhost:11434 - Integration code snippet for the target language (Python, Node.js, or cURL)
- Hardware assessment report: OS, RAM, GPU availability, recommended models
- Performance baseline: tokens per second for the selected model on local hardware
Show full SKILL.md (285 more words)Show less
Error Handling
| 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.
Examples
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.
Resources
- Ollama Official Documentation -- installation, model library, API reference
- Ollama Model Library -- available models with size and capability details
- Ollama Python Client -- Python SDK for local inference
- Ollama JavaScript Client -- Node.js SDK for local inference
- Hardware sizing guide: RAM requirements by model parameter count and quantization level
© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in skills/.curated/ollama-setup of jeremylongshore/tons-of-skills-marketplace.
- SKILL.md
- references/errors.md
- references/examples.md
- references/skill-workflow.md
Open the folder on GitHubat commit cfae287
Compare with similar skills
Ollama Setup next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ollama Setup this skilljeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.4k | Automated safety check: Notes | MIT | |
| Perfupraullenchai/Rapid-MLX | 4k | — | ~1.6k | Automated safety check: Notes | Custom licence | |
| Vllm Deploy Dockervllm-project/vllm-skills | 102 | — | ~2.5k | Automated safety check: Notes | Apache-2.0 | |
| Vllmmagnus919/agent-skills | 115 | — | ~4.1k | Automated safety check: Notes | MIT | |
| Deploynoskillish/bankmcp | 277 | — | ~744 | Automated safety check: Pass | MIT | |
| Agentdock User Guideuvwt/agentdock | 1.2k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 |
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Questions about Ollama Setup
What does Ollama Setup do?
Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Ollama Setup is an agent skill from jeremylongshore/tons-of-skills-marketplace. Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs.
When should I use Ollama Setup?
Ollama Setup fits situations like: needs local LLM deployment; free AI alternatives; wants to eliminate hosted API costs; phrases: install ollama.
How do I install Ollama Setup in Claude Code?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a claude-code`. Or copy the skill folder (skills/.curated/ollama-setup in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/ollama-setup in your project. Claude Code loads it when a task matches its description.
How do I install Ollama Setup in Codex?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a codex`. Or copy the skill folder (skills/.curated/ollama-setup in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/ollama-setup in your project. Codex loads it when a task matches its description.
Can I use Ollama Setup in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ollama-setup, .gemini/skills/ollama-setup, .github/skills/ollama-setup and .opencode/skills/ollama-setup in your project.
What does Ollama Setup need to run?
Going by SKILL.md and its folder, Ollama Setup needs the command-line tools its instructions call (ollama, brew, curl, sh and docker). Our summary lists: Python 3; Node.js; Docker. Its frontmatter pre-approves these tools: Read, Write, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.
Does Ollama Setup access the network?
SKILL.md names 2 domains. In commands or code: ollama.com; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.
Is Ollama Setup safe to install?
Our automated static check of SKILL.md found notes only (pipes a well-known installer script into a shell; runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Ollama Setup use?
Ollama Setup is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Ollama Setup use?
About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.1k tokens, read only when the agent opens those files.
What are the alternatives to Ollama Setup?
Skills that share tags, products or a category with Ollama Setup: Perfup (raullenchai/Rapid-MLX, 4k stars), Vllm Deploy Docker (vllm-project/vllm-skills, 102 stars), Vllm (magnus919/agent-skills, 115 stars) and Deploy (noskillish/bankmcp, 277 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ollama Setup?
jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.
Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.