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Local AI Stack

作者 jaysclawd-cloud · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install local-ai-stack
功能描述
Transform your Mac into an offline AI workstation with Ollama and OpenCode, running curated local models for coding and reasoning without internet or API costs.
使用说明 (SKILL.md)

SKILL.md — Local AI Stack

Purpose

Transform any Mac into a powerful offline AI workstation. Installs Ollama (local model runner) + OpenCode (terminal coding agent) with the best pre-selected models. Fully offline — no API costs, no internet required.

What You Get

  • Ollama — Local model runner (14GB models, ~$0 to run)
  • OpenCode — Terminal coding agent with free built-in models
  • 4 curated models — qwen2.5-coder, mistral, gemma3, llama3.2
  • Bi-weekly auto-updates — New models pulled automatically
  • OpenClaw integration — Works with your existing agent

Requirements

  • macOS (Apple Silicon recommended)
  • 24GB+ RAM (for larger models)
  • 50GB+ free disk space
  • Homebrew installed

Installation

Step 1: Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

Or download from: https://ollama.com/download

Step 2: Pull Models

ollama pull qwen2.5-coder    # Best for coding
ollama pull mistral          # Fast tasks
ollama pull gemma3          # Reasoning
ollama pull llama3.2        # General purpose

Step 3: Install OpenCode

brew install opencode

Step 4: Configure OpenCode

# Test free built-in model
opencode run "Hello" --model opencode/big-pickle

Usage

Ollama Commands

# Run a local model
ollama run qwen2.5-coder "Write a Python function..."

# List installed models
ollama list

# Pull latest model version
ollama pull qwen2.5-coder

# Remove a model
ollama rm mistral

OpenCode Commands

# Interactive coding session
opencode

# Single command
opencode run "Write a React component" --model opencode/big-pickle

# List available models
opencode models

# Help
opencode --help

Model Selection Guide

Model Size Best For
qwen2.5-coder 4.7GB Coding (primary)
mistral 4.4GB Fast responses
gemma3 3.3GB Reasoning
llama3.2 2.0GB General purpose

When to Use Local vs Cloud

Use Local When:

  • Offline (no internet)
  • Privacy-sensitive work
  • Quick coding tasks
  • Cost-sensitive (zero API fees)
  • Simple to medium complexity tasks

Use Cloud When:

  • Complex multi-step reasoning
  • Web search required
  • Long creative writing
  • Image generation
  • Advanced AI capabilities

Bi-Weekly Auto-Update

Add to cron for automatic model updates:

# Edit crontab
crontab -e

# Add this line (1st and 15th of each month at 9 AM)
0 9 1,15 * * /path/to/update-models.sh

Troubleshooting

Ollama won't start

# Check if running
ps aux | grep ollama

# Start manually
ollama serve

# Check logs
cat ~/.ollama/ollama.log

Model runs out of memory

  • Close other apps
  • Use smaller model (llama3.2 instead of qwen2.5-coder)
  • Check available RAM: top | head -20

OpenCode not found

# Find installation
which opencode

# Reinstall if needed
brew reinstall opencode

Files

  • Models stored: ~/.ollama/models/
  • Config: ~/.ollama/config.json
  • Logs: ~/.ollama/ollama.log

License

Ollama: MIT OpenCode: MIT

Author

Built with ❤️ for the OpenClaw community

Notes

  • Models load into RAM when used, unload when idle
  • Only one model runs at a time by default
  • For best performance, use Apple Silicon Mac with 24GB+ RAM
安全使用建议
This skill mostly does what it says: it installs Ollama/OpenCode and pulls local models. Before running anything, be aware of two things: (1) the README's claim "fully offline" is incorrect — you must fetch installers and model files from the Internet to set it up and to auto-update; (2) the install step uses 'curl ... | sh', which executes a live remote script on your machine — only run that if you trust the exact URL and have inspected the script. If you want true offline usage, download and verify installers and model files on a networked machine first, inspect any install scripts, and avoid or carefully audit the cron-based auto-update behavior so it doesn't later fetch unexpected content.
功能分析
Type: OpenClaw Skill Name: local-ai-stack Version: 1.0.0 The skill bundle (SKILL.md) instructs the agent to install an unverified third-party tool called 'opencode' and run a model named 'opencode/big-pickle'. The term 'pickle' is a well-known reference to Python's serialization format, which is notoriously vulnerable to Remote Code Execution (RCE). Additionally, the instructions suggest creating a cron job for persistence to run an external script ('update-models.sh') that is not provided in the bundle. While the stated purpose is a local AI stack, the combination of unknown binaries, suggestive naming of vulnerable components, and unverified persistence mechanisms poses a significant security risk.
能力评估
Purpose & Capability
The skill claims a "fully offline" workstation and "no internet required," yet the runtime instructions explicitly require network access to download and install Ollama, pull multiple models, and (optionally) auto-update them. The overall requested artifacts (Ollama, OpenCode, model downloads) are coherent with the name, but the offline claim is misleading.
Instruction Scope
SKILL.md gives concrete shell commands to run, references user-local files (~/.ollama/*), and suggests adding a cron job for auto-updates. It does not ask for unrelated files or credentials. However, it directs the user to execute networked install/pull steps and to schedule periodic updates — actions that have side effects beyond a one-time install and could fetch arbitrary remote content.
Install Mechanism
There is no formal install spec, but the instructions tell the user to run 'curl -fsSL https://ollama.com/install.sh | sh' (piping a remote script directly to sh) and to pull large models via 'ollama pull'. While the domain used (ollama.com) appears to be the official site, piping remote scripts to a shell is a high-risk pattern because it executes whatever the remote endpoint returns. Model downloads are unspecified and could fetch large binaries from remote hosts.
Credentials
The skill declares no required environment variables, no credentials, and no config paths beyond standard Ollama locations (~/.ollama). The requested access appears proportionate to installing and running local models: no secrets or unrelated credentials are requested.
Persistence & Privilege
The skill does not set always:true and does not autonomously install itself, but it instructs users to add a cron job to run an update script periodically. That creates persistent, scheduled network activity (model updates) under the user's account and should be reviewed before enabling.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install local-ai-stack
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /local-ai-stack 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Local AI Stack 1.0.0 — Turn your Mac into a fully offline AI workstation. - Install and run local large language models using Ollama and OpenCode with no internet required. - Curated set of 4 top models (qwen2.5-coder, mistral, gemma3, llama3.2) with a detailed usage and selection guide. - Step-by-step installation, setup, troubleshooting, and update instructions for macOS. - Bi-weekly automatic model updates via a simple cron job. - Designed for privacy, zero API costs, and seamless OpenClaw integration.
元数据
Slug local-ai-stack
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Local AI Stack 是什么?

Transform your Mac into an offline AI workstation with Ollama and OpenCode, running curated local models for coding and reasoning without internet or API costs. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 69 次。

如何安装 Local AI Stack?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install local-ai-stack」即可一键安装,无需额外配置。

Local AI Stack 是免费的吗?

是的,Local AI Stack 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Local AI Stack 支持哪些平台?

Local AI Stack 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Local AI Stack?

由 jaysclawd-cloud(@jaysclawd-cloud)开发并维护,当前版本 v1.0.0。

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