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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.
Installation
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install 0x-professor/ml-model-eval-benchmarkExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Instructions
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.
Informations sur la source
- Source
- github
- Repository
- openclaw/skills
- Path
- skills/0x-professor/ml-model-eval-benchmark
- Version
- —
- Commit
- 1726463af6394f1f1a7ec414b5e0d9170ee5c6b1
- Source de classification
- rules
- Statut
- active
- Visibilité
- public
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
Contenu extrait
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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