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Ml model eval benchmark

Skill 0x-Professor/Agent-Skills-Hub/skills/ml-model-eval-benchmark

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

Install
npx -y skills add 0x-Professor/Agent-Skills-Hub --skill ml-model-eval-benchmark

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 10 stars10 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 1 command, including `scripts/benchmark_models.py`.

SKILL.md

0.8 KB, 121 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it: 3 files

4.4 KB alongside SKILL.md, 1 of them executable

agents/

references/

scripts/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.