Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentscope-ai/OpenJudge --skill auto-arenagit clone --depth 1 https://github.com/agentscope-ai/OpenJudgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentscope-ai/openjudge/auto-arena)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/auto-arena"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/auto-arena/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/auto-arena"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/auto-arena.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 98 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 109 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 140 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 144 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 149 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.02472 |
| Opus 5 | $0.00051 | $0.01236 |
| Sonnet 5 | $0.00021 | $0.00494 |
| Haiku 4.5 | $0.00010 | $0.00247 |
Grade A, and why
auto-arena scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Arena Skill
End-to-end automated model comparison using the OpenJudge AutoArenaPipeline:
- Generate queries — LLM creates diverse test queries from task description
- Collect responses — query all target endpoints concurrently
- Generate rubrics — LLM produces evaluation criteria from task + sample queries
- Pairwise evaluation — judge model compares every model pair (with position-bias swap)
- Analyze & rank — compute win rates, win matrix, and rankings
- Report & charts — Markdown report + win-rate bar chart + optional matrix heatmap
Prerequisites
# Install OpenJudge
pip install py-openjudge
# Extra dependency for auto_arena (chart generation)
pip install matplotlib
Gather from user before running
| Info | Required? | Notes |
|---|---|---|
| Task description | Yes | What the models/agents should do (set in config YAML) |
| Target endpoints | Yes | At least 2 OpenAI-compatible endpoints to compare |
| Judge endpoint | Yes | Strong model for pairwise evaluation (e.g. gpt-4, qwen-max) |
| API keys | Yes | Env vars: OPENAI_API_KEY, DASHSCOPE_API_KEY, etc. |
| Number of queries | No | Default: 20 |
| Seed queries | No | Example queries to guide generation style |
| System prompts | No | Per-endpoint system prompts |
| Output directory | No | Default: ./evaluation_results |
| Report language | No | "zh" (default) or "en" |
Quick start
CLI
# Run evaluation
python -m cookbooks.auto_arena --config config.yaml --save
# Use pre-generated queries
python -m cookbooks.auto_arena --config config.yaml \
--queries_file queries.json --save
# Start fresh, ignore checkpoint
python -m cookbooks.auto_arena --config config.yaml --fresh --save
# Re-run only pairwise evaluation with new judge model
# (keeps queries, responses, and rubrics)
python -m cookbooks.auto_arena --config config.yaml --rerun-judge --save
Python API
import asyncio
from cookbooks.auto_arena.auto_arena_pipeline import AutoArenaPipeline
async def main():
pipeline = AutoArenaPipeline.from_config("config.yaml")
result = await pipeline.evaluate()
print(f"Best model: {result.best_pipeline}")
for rank, (model, win_rate) in enumerate(result.rankings, 1):
print(f"{rank}. {model}: {win_rate:.1%}")
asyncio.run(main())
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 275 lines · 103 tokens per session scan A d3b6fdbb3629
auto-arena is a skill published in the GitHub repository agentscope-ai/OpenJudge (826 stars, last pushed 3d ago), licensed Apache-2.0. It adds 103 tokens to every session and 2,472 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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