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ml-model-eval-benchmark
Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.
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。
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.pyto generate benchmark outputs. - Read
references/benchmarking-guide.mdfor 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.
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