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00-academic-router

Use when the user wants help with academic papers or citations but it's unclear which specific workflow fits — reviewing a paper, checking a BibTeX file for fake references, or benchmarking multiple LLMs on reference-recommendation accuracy. Also use when the user mentions paper review, peer review, BibTeX verification, citation checking, reference hallucination, or academic literature accuracy and hasn't specified which of those three tasks they mean. This skill is the entry router for the academic-eval suite: it asks one diagnostic question then routes to the right sub-skill.

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包含的文件(1)

  • SKILL.md3.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Academic Eval Router

Entry router for the academic-eval suite. You diagnose what the user actually wants and route them to one of three sub-skills. You don't review papers, verify BibTeX files, or run arena benchmarks 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, which of these matches what you want?

a) Review a single paper (PDF or LaTeX source) for correctness/quality/novelty
   — optionally also check its bibliography
b) Check a standalone .bib file for fabricated or mismatched references
   (no paper review needed)
c) Benchmark/compare multiple LLMs on how often they hallucinate references
   when asked to recommend citations (arena-style, many queries)

Shortcut rule: if the user already said "review my paper", "check this PDF", "verify this .bib file", or "compare models on reference hallucination", skip the question — the routing is already clear from their phrasing.

Triage Table

User says / hasUse workflowWhat it does
"Review this paper" (PDF or .tar.gz/.zip TeX source)01-paper-reviewMulti-stage review: safety, correctness, quality/novelty score, criticality — optionally + BibTeX check
"Review this paper AND check its references"01-paper-reviewSame pipeline with --bib set — one run covers both
"Just check this .bib file, no paper"02-bib-verifyCross-checks every entry against CrossRef/arXiv/DBLP, flags verified/suspect/not_found
"Compare N models on how often they cite fake papers" / "benchmark reference hallucination rate"03-ref-hallucination-arenaRuns many recommendation queries per model, verifies every returned reference, ranks models by hallucination rate
"Compare models on general quality/response, not specifically citations"—Not this suite — see the arena-eval suite's 01-auto-arena instead

Key distinctions

  • 01-paper-review vs 02-bib-verify: both use the same underlying cookbooks.paper_review pipeline. Use 01-paper-review whenever a paper file exists (even if the only thing the user cares about is the bibliography — --bib_only mode is documented there). Use 02-bib-verify only when there is no paper, just a loose .bib file to sanity-check.
  • 01-paper-review/02-bib-verify vs 03-ref-hallucination-arena: the first two evaluate one document's existing references after the fact. The third evaluates model behavior — how often a model invents fake citations when asked to recommend some, across a benchmark of queries and models. If the user wants a leaderboard/ranking of models, not a report on one document, route to 03-ref-hallucination-arena.

Output

Recommended workflow: `[skill-name]`

Why: [one sentence tying the user's request to the triage table row]

Recommend exactly one workflow. If the request spans two (e.g., "review this paper, and separately benchmark 3 models on citation accuracy"), say so explicitly and give both, in the order the user would naturally do them.

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同一仓库的技能

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.

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agentscope-ai/OpenJudge8872026年10月20日 更新

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.

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agentscope-ai/OpenJudge8872026年10月20日 更新

Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.

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agentscope-ai/OpenJudge8872026年10月20日 更新

Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.

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agentscope-ai/OpenJudge8872026年10月20日 更新

Verify a BibTeX file for hallucinated or fabricated references by cross-checking every entry against CrossRef, arXiv, and DBLP. Reports each reference as verified, suspect, or not found, with field-level mismatch details (title, authors, year, DOI). Use when the user wants to check a .bib file for fake citations, validate references in a paper, or audit bibliography entries for accuracy.

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agentscope-ai/OpenJudge8872026年10月20日 更新

Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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agentscope-ai/OpenJudge8872026年10月20日 更新

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