00-arena-router
Skill1.1k starsChanged 10 months agoApache-2.0View on GitHub
Use when the user wants to compare or benchmark multiple LLMs/agents arena-style but it's unclear which specific workflow fits — a general-purpose win-rate comparison on a custom task, or a benchmark specifically about reference/citation hallucination rate. Also use when the user mentions model arena, agent arena, pairwise model comparison, win-rate ranking, or comparing models on a task and hasn't specified whether that task is generic or about citation accuracy. This skill is the entry router for the arena-eval suite: it asks one diagnostic question when needed, then recommends the workflow or workflows needed to cover the request.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- agentscope-ai/OpenJudgeword for word, first seen Oct 25, 2026
Other versions1 project uses a version of this.
Word for word · 1
- agentscope-ai/OpenJudge★ 1.1k100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository20 other files it gives its agents.
- 00-academic-routerSkill
- 01-paper-reviewSkill
- 02-bib-verifySkill
- 03-ref-hallucination-arenaSkill
- 01-auto-arenaSkill
- 02-ref-hallucination-arenaSkill
- claude-authenticitySkill
- meta-evalSkill
- eval-designSkill
- metric-designSkill
- align-humanSkill
- eval-reportSkill
- rag-evalSkill
- prompt-regressionSkill
- redteamSkill
- bootstrapSkill
- find-skills-comboSkill
- mmx-cliSkill
- 01-graders-and-pipelineSkill
- 02-rl-rewardSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 27
Ask (unless the user's request already makes the answer obvious):
- Distinctive: line 38
Shortcut rule: if the user already said "run an arena eval on my chatbot task" or "benchmark reference hallucination across these models", …
- Very specific: line 67
If the user cares only about citation accuracy, prefer 02-ref-hallucination-arena over 01-auto-arena even if they phrase it as "which model …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Arena Eval Router
Entry router for the arena-eval suite. You diagnose what the user wants to
compare models on and route them to the appropriate sub-skill or both when
the request spans both evaluation goals. You don't run comparisons yourself
— you're the triage desk.
Each sub-skill is self-contained: it carries inline everything it needs, so it can be installed and used on its own.
Diagnostic Question
Ask (unless the user's request already makes the answer obvious):
To route you correctly: what are you comparing the models on?
a) A custom task of your own choosing (chatbot quality, summarization,
coding, anything) — you'll get win-rate rankings from a judge model
b) Specifically how often each model fabricates or hallucinates references
when asked to recommend citationsShortcut rule: if the user already said "run an arena eval on my chatbot task" or "benchmark reference hallucination across these models", skip the question — the routing is already clear from their phrasing. Also skip the question when they explicitly ask for both general quality and citation accuracy; recommend both workflows.
Triage Table
| User says / has | Use workflow | What it does |
| "Compare/benchmark/rank these models on [any custom task]" | 01-auto-arena | Generates queries from a task description, collects responses, auto-generates rubrics, runs pairwise judge comparisons, produces win-rate rankings |
| "Which model hallucinates citations least?" / "benchmark reference recommendation accuracy" | 02-ref-hallucination-arena | Runs reference-recommendation queries per model, verifies every returned citation against CrossRef/PubMed/arXiv/DBLP, ranks by verified accuracy |
| "Compare general helpfulness AND citation accuracy" | 01-auto-arena, then 02-ref-hallucination-arena | Runs separate evaluations for judge preference and verified citation accuracy, preserving both goals |
| "I want to review one paper's existing bibliography, not compare models" | — | Not this suite — see the academic-eval suite's 01-paper-review / 02-bib-verify instead |
Key distinction
Both workflows produce model rankings from head-to-head-style evaluation, but differ in what "correct" means:
01-auto-arena: correctness is judge opinion — an LLM judge scores pairwise which response is better for an arbitrary task. Works for any task, needs no ground truth.02-ref-hallucination-arena: correctness is externally verifiable — every cited reference is checked against real bibliographic databases (CrossRef/PubMed/arXiv/DBLP), so the ranking reflects factual accuracy, not judge preference. Narrower scope (citation recommendation only) but higher ground-truth confidence.
If the user cares only about citation accuracy, prefer
02-ref-hallucination-arena over 01-auto-arena even if they phrase it as
"which model is better."
Output
Recommended workflow: `[skill-name]`
Why: [one sentence tying the user's request to the triage table row]Recommend one workflow when it covers the request. If the user asks for both
general quality and citation accuracy, recommend 01-auto-arena followed by
02-ref-hallucination-arena as separate runs (or follow the user's requested
order). Explain that the two runs measure different things and report their
results separately; neither ranking substitutes for the other.
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