Individual skill
01-auto-arena
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.
skills/arena-eval/01-auto-arena/SKILL.md
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- alignment Repository topic
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# Auto Arena Skill
End-to-end automated model comparison using the OpenJudge `AutoArenaPipeline`:
1. **Generate queries** — LLM creates diverse test queries from task description
2. **Collect responses** — query all target endpoints concurrently
3. **Generate rubrics** — LLM produces evaluation criteria from task + sample queries
4. **Pairwise evaluation** — judge model compares every model pair (with position-bias swap)
5. **Analyze & rank** — compute win rates, win matrix, and rankings
6. **Report & charts** — Markdown report + win-rate bar chart + optional matrix heatmap
## Prerequisites
```bash