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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
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install 0x-professor/ml-model-eval-benchmarkInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Skill 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.
Source information
- Source
- github
- Repository
- openclaw/skills
- Path
- skills/0x-professor/ml-model-eval-benchmark
- Version
- —
- Commit
- 1726463af6394f1f1a7ec414b5e0d9170ee5c6b1
- Classification source
- rules
- Status
- active
- Visibility
- public
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
Extracted content
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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