📦 MetriLLM — MetriLLM工具
v0.2.11[AI辅助] Find the best local LLM for your machine. Tests speed, quality and RAM fit, then tells you if a model is worth running on your hardware.
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by 安全扫描
OpenClaw
安全
high confidenceThe skill's requirements and instructions are coherent with its stated purpose (benchmarking local LLMs); nothing requested is disproportionate, though the user should review the npm package and the optional leaderboard sharing before installing.
评估建议
This skill is coherent for benchmarking local LLMs, but take two precautions before installing: (1) review the npm package / GitHub repo (https://github.com/MetriLLM/metrillm) or audit the package contents before running npm install -g, since global npm installs execute third-party code on your machine; (2) only use --share if you consent to publishing model names, scores and hardware details (the README says no personal data is sent, but verify what the package actually uploads). Also ensure yo...详细分析 ▾
✓ 用途与能力
The name/description match the instructions: it tells you how to install the metrillm CLI, requires Node 20+ and a local LLM server (Ollama or LM Studio), and runs benchmarking commands. No unrelated credentials, binaries, or config paths are requested.
ℹ 指令范围
Instructions stay within the benchmarking scope (run metrillm bench, view local ~/.metrillm/results/). One caution: the optional --share command uploads results (model name, scores, hardware specs) to metrillm.dev; the SKILL.md states no personal data is sent, but that claim cannot be verified from instructions alone. The skill does not instruct access to unrelated files or env vars.
ℹ 安装机制
Installation is via npm (npm install -g metrillm) which is a standard delivery for a Node CLI. npm installs are moderate-risk because they execute third-party code on your system; this is proportionate to the stated purpose but you should inspect the package or source repository before global installation.
✓ 凭证需求
No environment variables, credentials, or config paths are required. The only data potentially exported is from the explicit --share action (model, scores, hardware specs), which is reasonable for a community leaderboard.
✓ 持久化与权限
The skill is not always-enabled and is user-invocable. It does not request persistent elevated privileges or modify other skills. Autonomous invocation is permitted by default (normal), but nothing in the skill attempts to gain extra persistence.
安全有层次，运行前请审查代码。
运行时依赖
无特殊依赖
版本
latestv0.2.112026/3/3
Fix license: Apache-2.0, not MIT
● 可疑
安装命令
点击复制官方npx clawhub@latest install metrillm
镜像加速npx clawhub@latest install metrillm --registry https://cn.longxiaskill.com镜像同步中
技能文档
Test any local model and get a clear verdict: is it worth running on your machine?
Prerequisites
- 节点.js 20+ — check 带有
节点 -v - Ollama 或 LM Studio installed 和 running
ollama serve
- LM Studio: lmstudio.ai, 加载 模型 和 开始 server
- MetriLLM CLI — install globally:
npm install -g metrillm
Usage
列表 可用 models
ollama list
Run 满 benchmark
metrillm bench --model $ARGUMENTS --json
This measures:
- Performance: tokens/第二个, 时间 到 第一个 令牌, memory usage
- Quality: reasoning, math, coding, instruction following, structured 输出, multilingual
- Fitness verdict: EXCELLENT / GOOD / MARGINAL / 不 RECOMMENDED
Performance-仅 benchmark (faster)
metrillm bench --model $ARGUMENTS --perf-only --json
Skips quality evaluation — measures speed and memory only.
视图 上一个 results
ls ~/.metrillm/results/
Read any JSON file to see full benchmark details.
分享 到 公开 leaderboard
metrillm bench --model $ARGUMENTS --share
Uploads your result to the MetriLLM community leaderboard — an open, community-driven ranking of local LLM performance across real hardware. Compare your results with others and help the community find the best models for every setup. Shared data includes: model name, scores, hardware specs (CPU, RAM, GPU). No personal data is sent.
Interpreting Results
| Verdict | Score | Meaning |
|---|---|---|
| EXCELLENT | >= 80 | Fast and accurate — great fit |
| GOOD | >= 60 | Solid — suitable for most tasks |
| MARGINAL | >= 40 | Usable but with tradeoffs |
| NOT RECOMMENDED | < 40 | Too slow or inaccurate |
tokensPerSecond> 30 = good 对于 interactive 使用ttft< 500ms = responsivememoryUsedGBvs 可用 RAM = 将 fit?
Tips
- 使用
--perf-仅对于 quick tests - 关闭 GPU-intensive apps 之前 benchmarking
- Benchmark 持续时间 varies depending 在...上 模型 speed 和 响应 length
打开 Source
MetriLLM is free and open source (Apache 2.0). Contributions, issues, and feedback are welcome: github.com/MetriLLM/metrillm