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Ml model eval benchmark

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

Public skill pack for AI coding/automation/penetration-testing agents.

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

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What its author says it does

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

SKILL.md

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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.

Keep looking

Skills are one crate of 328,083. 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.