Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AutoResearch-Factory/Agon --skill novelty-checkgit clone --depth 1 https://github.com/AutoResearch-Factory/AgonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/autoresearch-factory/agon/novelty-check)<a href="https://agentmods.dev/skills/autoresearch-factory/agon/novelty-check"><img src="https://agentmods.dev/badge/skills/autoresearch-factory/agon/novelty-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/autoresearch-factory/agon/novelty-check"><img src="https://agentmods.dev/badge/skills/autoresearch-factory/agon/novelty-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.00573 |
| Opus 5 | $0.00032 | $0.00287 |
| Sonnet 5 | $0.00013 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
novelty-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 53 lines · 63 tokens per session scan A 8d28a17693aa
novelty-check is a skill published in the GitHub repository AutoResearch-Factory/Agon (47 stars, last pushed 6d ago), licensed MIT. It adds 63 tokens to every session and 573 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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