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Self Hosted Ai
作者
Twin Geeks
· GitHub ↗
· v1.0.2
· MIT-0
133
总下载
2
收藏
2
当前安装
3
版本数
在 OpenClaw 中安装
/install self-hosted-ai
功能描述
Self-hosted AI — run your own LLM inference, image generation, speech-to-text, and embeddings. No cloud APIs, no SaaS subscriptions, no data leaving your net...
安全使用建议
This skill appears to be a legitimate self-hosted router for local LLMs, but several red flags mean you should proceed cautiously. Before installing: (1) review the 'ollama-herd' PyPI package and the linked GitHub repository source to confirm what code will run during 'pip install' and at runtime; (2) confirm what 'herd-node' does — especially its LAN auto-discovery behavior — and whether you are comfortable with it probing your local network; (3) check where it will write files (the SKILL.md references ~/.fleet-manager) and ensure that is acceptable or run in an isolated VM/container; (4) verify the origin and trustworthiness of the 'uv' tool and the model backends it installs (they may download large binaries from third parties); (5) avoid installing on sensitive machines or accounts with high privileges; and (6) if you want to proceed, prefer testing in a sandboxed environment (VM/container) and inspect network traffic during initial runs. If you can provide the PyPI project source or a link to the exact package release, I can re-evaluate and raise/lower confidence.
能力评估
Purpose & Capability
The name/description match the instructions (run a self-hosted router, local LLM/image/speech endpoints). However the SKILL.md expects you to pip install 'ollama-herd', run 'herd'/'herd-node', and use an 'uv' tool to install model backends — yet the registry metadata only declares curl/wget as required binaries and lists no required config paths or env vars. The SKILL.md embeds metadata that refers to '~/.fleet-manager' config files but the registry lists none. These mismatches make it unclear what actual system access and tools the skill expects.
Instruction Scope
Instructions tell the agent/user to pip install a PyPI package, run local services (herd, herd-node) which auto-discover devices on the local network, and curl localhost endpoints. Those actions are coherent with a fleet router but imply network scanning/auto-discovery and writing runtime config/logs under the home directory. The SKILL.md does not explicitly warn that 'herd-node' will probe the LAN or create files under ~/.fleet-manager.
Install Mechanism
There is no formal install spec in the registry; the SKILL.md instructs use of 'pip install ollama-herd' (PyPI). pip installation runs arbitrary code and can modify the system; the skill also instructs running 'uv tool install ...' which may download/execute additional binaries or model weights. No integrity/release URLs or alternative verified install methods are provided.
Credentials
The skill declares no required environment variables or primary credentials (which is appropriate). But it will request network access (local LAN auto-discovery), create config/log files under ~/.fleet-manager (present in SKILL.md metadata), and may download large model artifacts — all of which are broader privileges than a simple local curl/wget requirement would suggest.
Persistence & Privilege
The registry flags are reasonable: always=false and user-invocable=true. The skill will likely create persistent local state (service, config files, logs) when you run the recommended installer/agents, but it does not request forced always-on inclusion in agents.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install self-hosted-ai - 安装完成后，直接呼叫该 Skill 的名称或使用
/self-hosted-ai触发 - 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
v1.0.2
Cross-platform support: macOS, Linux, and Windows. Updated OS metadata, descriptions, and hardware recommendations.
v1.0.1
- Expanded multilingual description to include Chinese and Spanish.
- Revised all documentation and examples to emphasize "self-hosted" benefits and usage throughout.
- Clarified that all requests, processing, and guardrails operate fully within local, self-hosted infrastructure, with no cloud dependencies.
- Updated cost comparisons and feature tables to specify "self-hosted" alternatives for each cloud service.
- Improved clarity and consistency in instructions, replacing generic terms with "self-hosted" across guides and usage notes.
v1.0.0
- Initial release of self-hosted-ai (v1.0.0).
- Enables self-hosted LLM inference, image generation, speech-to-text, and embeddings on Mac and Linux devices.
- Serves as a local alternative to OpenAI, DALL-E, Whisper API, and cloud embedding services.
- Provides a router for automatic load balancing and device discovery across your local network.
- No cloud APIs, SaaS subscriptions, or data leaving your hardware—data sovereignty and zero per-request costs.
- Easy install with `pip`, no Docker or Kubernetes required.
元数据
常见问题
Self Hosted Ai 是什么？
Self-hosted AI — run your own LLM inference, image generation, speech-to-text, and embeddings. No cloud APIs, no SaaS subscriptions, no data leaving your net... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 133 次。
如何安装 Self Hosted Ai？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install self-hosted-ai」即可一键安装，无需额外配置。
Self Hosted Ai 是免费的吗？
是的，Self Hosted Ai 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Self Hosted Ai 支持哪些平台？
Self Hosted Ai 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（darwin, linux, windows）。
谁开发了 Self Hosted Ai？
由 Twin Geeks（@twinsgeeks）开发并维护，当前版本 v1.0.2。
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