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AutoResearch-FactoryAutoResearch-Factory/Agon/skills/novelty-check/SKILL.md

novelty-check

Skill54 starsChanged 4 months agoMITView on GitHub

Use when checking novelty of a research idea or proposal. Extract core claims, do multi-source arxiv / Scholar / S2 search, grade each claim HIGH/MEDIUM/LOW. 基本就是 ARIS novelty-check skill, 但去掉了 cross-model verification 和 report schema.

12 other projects use a version of this, 9 of them edited.

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Projects with it also use research-refine, research-refine-pipeline and 7 more.

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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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  • +6 more from raja21068

The file

We split this skill into the rules it gives an agent

4 rules. 4 are common, 0 are distinctive, and 0 are very specific to this project.

  • 4

    Common

    Most of the 11 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Use specific technical terms from the claim

    line 26
  2. Common:

    Always check the most recent 6 months of arXiv — the field moves fast

    line 28
  3. Common:

    Check both the method AND the experimental setting for novelty.

    line 43
  4. Common:

    If the method is not novel but the FINDING would be, say so explicitly.

    line 44

“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “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
<novelty-check>

这是一个挑剔的research reviewer系统性检查一个工作的核心贡献是否新颖的流程.

流程

1. Extract Core Claims

Identify 3-5 core technical claims that would need to be novel:

  • What is the method?
  • What problem does it solve?
  • What is the mechanism?
  • What makes it different from obvious baselines?

2. Multi-Source Literature Search

For each core claim, search broadly:

  • sources: arXiv / Google Scholar / Semantic Scholar
  • Use specific technical terms from the claim
  • Try at least 3 different query formulations per claim
  • Always check the most recent 6 months of arXiv — the field moves fast
  • 顶会近期核查: 明确看最近的 ICLR, NeurIPS, ICML 等会议的相关子领域的 proceedings 和 accepted papers

3. 判断 novelty

对每个 claim 打一个 novelty 等级:

  • HIGH: 没查到实质重叠的工作, claim 是原创机制
  • MEDIUM: 存在相关工作, 但 application / scope / mechanism 有明显区别
  • LOW: 已有高度相似工作, 或本质上是 "Apply X to Y" 且 application 没揭示意外洞见

Key Rules

  • Be brutally honest — false novelty claims waste months of research time.
  • "Applying X to Y" is NOT novel unless the application reveals surprising insights.
  • Check both the method AND the experimental setting for novelty.
  • If the method is not novel but the FINDING would be, say so explicitly.
  • web search / metadata 摘要只是线索, 不是证据. 所有进入报告的论文引用必须用 arxiv tool / arxiv skill 下载并阅读 tex/full text 核验.
  • 最终报告 novelty check 部分的字数在 200-300 words 左右.

不要在此停下

novelty check 只是某个 reviewer workflow 的第 1 步. 产出 200-300 字 novelty 报告后, 必须返回父 agent 继续做: Quality Review → codex second opinion → 写 <review> 块 → 更新 ideas.xml / proposals.xml. 不要把 novelty check 的报告当成最终交付物而 end_turn.

</novelty-check>

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