# Ml Model Eval Benchmark

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

- Skill: `majiayu000/ml-model-eval-benchmark` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/ml-model-eval-benchmark`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/ml-model-eval-benchmark/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/ml-model-eval-benchmark

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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.