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modelscopemodelscope/OpenJudge/skills/arena-eval/00-arena-router/SKILL.md

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.

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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
  1. Distinctive:

    Ask (unless the user's request already makes the answer obvious):

    line 27
  2. Distinctive:

    Shortcut rule: if the user already said "run an arena eval on my chatbot task" or "benchmark reference hallucination across these models", …

    line 38
  3. Very specific:

    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 …

    line 67

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 / hasUse workflowWhat it does
"Compare/benchmark/rank these models on [any custom task]"01-auto-arenaGenerates 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-arenaRuns 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-arenaRuns 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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