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不碰外部(只输出文字)无严重或高危命中agentscope-ai/OpenJudge

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技能内容

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 citations

Shortcut 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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