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Install in OpenClaw
/install hardware-llm-optimizer-v2
Description
AI硬件LLM推荐工具 - 基于llmfit内核。自动检测CPU/GPU/RAM/VRAM → 智能推荐最适合的大模型 + 量化方案 + 速度估算。支持100+模型库，内置TUI界面和硬件模拟。
README (SKILL.md)
Hardware LLM Optimizer v2.0
基于 llmfit 的智能硬件LLM推荐工具
安装状态
llmfit 已安装在: /usr/local/bin/llmfit
快速使用
当用户问"能跑什么大模型"、"推荐LLM"、"硬件检测"时使用：
1. 查看推荐模型
llmfit recommend
2. 查看所有推荐（JSON格式，便于解析）
llmfit recommend --json
3. 按用途筛选
llmfit recommend --use-case coding
llmfit recommend --use-case chat
llmfit recommend --use-case general
llmfit recommend --use-case embedding
4. 硬件模拟（模拟不同配置）
# 模拟 16GB 显存
llmfit recommend --memory 16G
# 模拟 32GB 显存 + 64GB RAM
llmfit recommend --memory 32G --ram 64G
5. 交互式TUI（需要终端）
llmfit
输出字段说明
| 字段 | 含义 |
|---|---|
name |
模型名称 |
parameter_count |
参数量 |
best_quant |
推荐量化方案 |
score |
综合评分（越高越好） |
estimated_tps |
预估速度（tok/s） |
memory_required_gb |
所需显存 |
run_mode |
运行模式（GPU/CPU/MoE） |
fit_level |
匹配度（Perfect/Good/Marginal） |
量化方案参考
| 量化 | 质量 | 速度 | 适用场景 |
|---|---|---|---|
| FP16 | 最高 | 最慢 | 大显存GPU |
| Q8_0 | 很高 | 较快 | 中等显存 |
| Q6_K | 高 | 快 | 6-8GB显存 |
| Q4_K_M | 中高 | 最快 | 4-6GB显存 |
| Q2_K | 中 | 最快 | \x3C4GB显存 |
本地运行命令
安装Ollama模型
ollama run \x3Cmodel-name>
使用llama.cpp
# 下载GGUF后
./llama.cpp -m \x3Cmodel.gguf> --rompt \x3Cprompt>
最低配置参考（来自llmfit）
| 显存 | 推荐模型 | 量化 |
|---|---|---|
| 2GB | Phi-3-mini, Gemma-2B | Q4 |
| 4GB | Llama3-8B, Qwen-7B | Q4 |
| 6GB | Llama2-13B, Mistral-7B | Q4/Q6 |
| 8GB | Llama2-13B, Yi-9B | Q5/Q6 |
| 12GB | Llama2-34B | Q4 |
| 16GB | Llama2-34B, Qwen-72B | Q4 |
| 24GB+ | 70B大模型 | Q4/Q8 |
安装llmfit（如需）
curl -fsSL https://llmfit.axjns.dev/install.sh | sh
优势对比
| 功能 | v1.0 | v2.0 (llmfit) |
|---|---|---|
| 模型库 | 手动查表 | 100+自动匹配 |
| 量化推荐 | 简单估算 | 智能最优 |
| 速度估算 | ❌ | ✅ |
| 下载源 | ❌ | ✅ GGUF |
| 硬件模拟 | ❌ | ✅ |
| TUI界面 | ❌ | ✅ |
| 多GPU | ❌ | ✅ |
| MoE支持 | ❌ | ✅ |
Powered by llmfit | Updated: 2026-04-17
Usage Guidance
This skill's detection code (detect.py) appears benign and aligned with the description: it inspects local hardware via psutil and nvidia-smi and prints recommendations. However, the SKILL.md asks you to install 'llmfit' by piping a script from https://llmfit.axjns.dev/install.sh directly into sh — that pattern is high-risk because it runs arbitrary code from an unvetted host. Before installing or running anything: (1) do NOT run curl ... | sh without inspecting the script; fetch the URL and review its contents first; (2) prefer installing llmfit from a known official source (GitHub releases, vendor homepage) or verify the domain and script integrity; (3) run detect.py locally in a restricted environment if you only want hardware info (it has no network calls); (4) ensure you understand any model downloads (GGUF/ollama) and avoid running unfamiliar binaries as root. If the maintainer can provide a verified upstream URL (official project repo/releases) or an explicit install spec using a reputable package host, that would reduce risk and could change this assessment.
Capability Analysis
Type: OpenClaw Skill
Name: hardware-llm-optimizer-v2
Version: 2.0.0
The skill bundle provides hardware detection and LLM recommendations. While the Python script (detect.py) is benign and uses standard libraries like psutil to gather system specs, the SKILL.md file contains a high-risk 'curl | sh' command to install a dependency from an external domain (llmfit.axjns.dev). This pattern is a known vector for supply chain attacks and remote code execution; although it is presented as a legitimate installation step, it constitutes a meaningful high-risk behavior that warrants a suspicious classification.
Capability Assessment
Purpose & Capability
The skill claims to detect hardware and recommend LLMs/quantization, and the included detect.py implements that behavior (psutil, nvidia-smi checks, /proc/version). However, the SKILL.md centers runtime usage on an external tool 'llmfit' (commands like 'llmfit recommend') while the package provides no install mechanism for llmfit and even asserts llmfit is already at /usr/local/bin; relying on an external binary that isn't provided weakens coherence.
Instruction Scope
Runtime instructions and detect.py stay within the stated purpose: they inspect local system state (CPU, RAM, nvidia-smi, /proc/version), produce recommendations, and reference running local model runtimes (ollama, llama.cpp). The SKILL.md does suggest running network-facing installs and model downloads, but it does not instruct reading unrelated secrets or sending detected data to external endpoints.
Install Mechanism
There is no formal install spec, but SKILL.md recommends installing llmfit with: curl -fsSL https://llmfit.axjns.dev/install.sh | sh. That is a direct download-and-execute from an unrecognized domain (axjns.dev) — high-risk practice. The skill itself does not include code to fetch that URL, but recommending it without provenance is disproportionate and potentially dangerous.
Credentials
The skill requests no environment variables, no credentials, and detect.py only queries local system info. There is no inappropriate credential access requested.
Persistence & Privilege
The skill does not request always:true and is not marked to be force-included. It does not attempt to modify other skills or system-wide configs. Normal autonomous invocation is allowed (platform default).
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install hardware-llm-optimizer-v2 - After installation, invoke the skill by name or use
/hardware-llm-optimizer-v2 - Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.0.0
Hardware LLM Optimizer v2.0 brings a major upgrade with intelligent, automated model recommendations based on your hardware.
- Now uses the llmfit core: auto-detects CPU/GPU/RAM/VRAM to recommend optimal large models and quantization.
- Expanded to support 100+ model libraries, with built-in TUI interface and hardware simulation features.
- Provides speed estimation, usage-based model filtering, and more detailed output (including quantization plans and fit scores).
- Supports advanced scenarios: multi-GPU, MoE, and local GGUF model sources.
- Improved user experience with clearer guides, command examples, and comparison to previous version.
Metadata
Frequently Asked Questions
What is Hardware LLM Optimizer v2 (llmfit)?
AI硬件LLM推荐工具 - 基于llmfit内核。自动检测CPU/GPU/RAM/VRAM → 智能推荐最适合的大模型 + 量化方案 + 速度估算。支持100+模型库，内置TUI界面和硬件模拟。 It is an AI Agent Skill for Claude Code / OpenClaw, with 72 downloads so far.
How do I install Hardware LLM Optimizer v2 (llmfit)?
Run "/install hardware-llm-optimizer-v2" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Hardware LLM Optimizer v2 (llmfit) free?
Yes, Hardware LLM Optimizer v2 (llmfit) is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Hardware LLM Optimizer v2 (llmfit) support?
Hardware LLM Optimizer v2 (llmfit) is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Hardware LLM Optimizer v2 (llmfit)?
It is built and maintained by SMS (@smseow001); the current version is v2.0.0.
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