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local-llm-ops — Claude Code skill: local LLMs with Ollama

A skill for running local models through Ollama on Apple Silicon: bring up the service, pull models, start chat sessions and benchmarks.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add bobmatnyc/claude-mpm-skills --skill local-llm-ops -a claude-code
Open on GitHub

What local-llm-ops does

The skill lays out the whole path for local LLM work in the localLLM repo: install Ollama, start the service, initialize the virtualenv, pull models, then open a chat or run measurements. Two commands get you going — ./setup_chatbot.sh followed by ./chatllm. With no models on disk, ollama pull mistral fixes that.

Setup and diagnostic scripts ship alongside CLI chat launchers that take task modes, such as ./chat -t coding -m codellama:70b. Benchmarks live in scripts/run_benchmarks.sh and read benchmarks/prompts.yaml plus benchmarks/models.yaml. Running ./install_aliases.sh gives you llm, llm-code and llm-fast. The virtualenv sits in .venv, configs and sessions under ~/.localllm/, and the Ollama API answers on http://localhost:11434.

Nothing works until Ollama is installed and its service is up: brew install ollama, then brew services start ollama. Confirm it with curl http://localhost:11434/api/version. When setup breaks, ./diagnose.sh narrows it down; the usual culprits are ollama missing from PATH or zero pulled models.

What it helps with

  • bring up Ollama and its service on Apple Silicon from scratch
  • pull a model and start a terminal chat
  • benchmark several models against a prompt set
  • switch chat task modes for code, prose and analysis
  • find out why setup fails using the diagnostic script

Example prompts for the agent

  • Run ./setup_chatbot.sh and start a local chat
  • Benchmark the models listed in benchmarks/models.yaml
  • The chat is not responding — run ./diagnose.sh and tell me what broke

How to install local-llm-ops

1Skills CLI (Vercel)

npx skills add bobmatnyc/claude-mpm-skills --skill local-llm-ops -a claude-code

2Manually: put the files in place

~/.claude/skills/local-llm-ops/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/local-llm-ops/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about local-llm-ops

How do I install local-llm-ops in Claude Code?

Run npx skills add bobmatnyc/claude-mpm-skills --skill local-llm-ops -a claude-code; the skill file lands at ~/.claude/skills/local-llm-ops/SKILL.md.

What does local-llm-ops do?

It covers local LLM operations with Ollama: environment setup, service startup, model pulls, chat launchers, benchmarks and diagnostics.

What do I need before using it?

Ollama installed with the service running, and at least one model pulled — ollama pull mistral, for instance.

Source text

SKILL.md · MIT
---
name: local-llm-ops
description: Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.
user-invocable: false
disable-model-invocation: true
version: 1.0.0
category: toolchain
author: Claude MPM Team
license: MIT
progressive_disclosure:
  entry_point:
    summary: "Run local LLMs with Ollama: setup venv, start service, pull models, launch chat, benchmark, and diagnose."
    when_to_use: "Operating local LLMs on macOS, running Ollama-based chat sessions, or benchmarking models for speed/latency."
    quick_start: "1. ./setup_chatbot.sh 2. ./chatllm 3. ollama pull mistral (if no models)"
tags:
  - llm
  - ollama
  - local
  - benchmark
  - chat
  - ops
---

# Local LLM Ops (Ollama)

## Overview

Your `localLLM` repo provides a full local LLM toolchain on Apple Silicon: setup scripts, a rich CLI chat launcher, benchmarks, and diagnostics. The operational path is: install Ollama, ensure the service is running, initialize the venv, pull models, then launch chat or benchmarks.

## Quick Start

```bash
./setup_chatbot.sh
./chatllm
```

If no models are present:

```bash
ollama pull mistral
```

## Setup Checklist

1. Install Ollama: `brew install ollama`
2. Start the service: `brew services start ollama`
3. Run setup: `./setup_chatbot.sh`
4. Verify service: `curl http://localhost:11434/api/version`

## Chat Launchers

- `./chatllm` (primary launcher)
- `./chat` or `./chat.py` (alternate launchers)
- Aliases: `./install_aliases.sh` then `llm`, `llm-code`, `llm-fast`

Task modes:

```bash
./chat -t coding -m codellama:70b
./chat -t creative -m llama3.1:70b
./chat -t analytical
```

## Benchmark Workflow

Benchmarks are scripted in `scripts/run_benchmarks.sh`:

```bash
./scripts/run_benchmarks.sh
```

This runs `bench_ollama.py` with:

- `benchmarks/prompts.yaml`
- `benchmarks/models.yaml`
- Multiple runs and max token limits

## Diagnostics

Run the built-in diagnostic script when setup fails:

```bash
./diagnose.sh
```

Common fixes:

- Re-run `./setup_chatbot.sh`
- Ensure `ollama` is in PATH
- Pull at least one model: `ollama pull mistral`

## Operational Notes

- Virtualenv lives in `.venv`
- Chat configs and sessions live under `~/.localllm/`
- Ollama API runs at `http://localhost:11434`

## Related Skills

- `toolchains/universal/infrastructure/docker`

© Claude MPM Team · MIT · bobmatnyc/claude-mpm-skills

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