Agent skill · AI & Agents

ml-model-eval-benchmark

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

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill ml-model-eval-benchmark --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 1 KB
Bundled scripts: none
Path: skills/ai-ml/ml-model-eval-benchmark/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the 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.

What's inside
Steps it walks through
  1. Overview
  2. Workflow
  3. Use Bundled Resources
  4. Guardrails
Ships with 1 file
  • metadata.json
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About this skill
What does the ml-model-eval-benchmark skill do?

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

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill ml-model-eval-benchmark --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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