OpenClaw

ml-model-eval-benchmark

Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.

0x-professor0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install 0x-professor/ml-model-eval-benchmark

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

ML Model Eval Benchmark

Overview

Produce consistent model ranking outputs from metric-weighted evaluation inputs.

Workflow

Define metric weights and accepted metric ranges.
Ingest model metrics for each candidate.
Compute weighted score and ranking.
Export leaderboard and promotion recommendation.

Use Bundled Resources

Run scripts/benchmark_models.py to generate benchmark outputs.
Read references/benchmarking-guide.md for weighting and tie-break guidance.

Guardrails

Keep metric names and scales consistent across candidates.
Record weighting assumptions in the output.

来源信息

Source: github
Repository: openclaw/skills
Path: skills/0x-professor/ml-model-eval-benchmark
Version: —
Commit: 1726463af6394f1f1a7ec414b5e0d9170ee5c6b1
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# ML Model Eval Benchmark

## Overview

Produce consistent model ranking outputs from metric-weighted evaluation inputs.

## Workflow

1. Define metric weights and accepted metric ranges.
2. Ingest model metrics for each candidate.
3. Compute weighted score and ranking.
4. Export leaderboard and promotion recommendation.

## Use Bundled Resources

- Run `scripts/benchmark_models.py` to generate benchmark outputs.
- Read `references/benchmarking-guide.md` for weighting and tie-break guidance.

## Guardrails

- Keep metric names and scales consistent across candidates.
- Record weighting assumptions in the output.

搜索