0x-professor/agent-skills-hub/ml-model-eval-benchmark

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

Install

npx vskill@latest install 0x-professor/agent-skills-hub/ml-model-eval-benchmark

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.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
11
Repository forks
2
7-day trend score
12

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