Overview
- Skill Key
- 0x-professor/ml-model-eval-benchmark
- Author
- 0x-professor
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/0x-professor/ml-model-eval-benchmark
- Latest Commit SHA
- 1726463af6394f1f1a7ec414b5e0d9170ee5c6b1
Official OpenClaw
rules
15%
ml-model-eval-benchmark
Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.
Stars
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Installs
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Status
ACTIVE
Visibility
PUBLIC
安装方式
直接复制以下提示词，发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店，若未安装，请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店，然后安装 ml-model-eval-benchmark 技能。 若已安装，则直接安装 ml-model-eval-benchmark 技能。
Extracted Content
SKILL.md excerpt
# 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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