research-novelty-audit

Audit AI research ideas for novelty and reviewer concerns against prior work.

Updated Apr 23, 2026

Search Tags:#ai #critical-thinking #peer-review #novelty #framing #prior-work

Ready to run

npx skills add https://github.com/Ocean326/Agents --skill research-novelty-audit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-novelty-audit
Source: https://github.com/Ocean326/Agents/tree/main/skills/global/research-novelty-audit
Command: npx skills add https://github.com/Ocean326/Agents --skill research-novelty-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

评估 AI、深度学习和序列表征学习 idea 或 draft 的新颖性与竞争定位。当 Codex 需要对比 prior work、压力测试贡献主张、提前暴露 reviewer 质疑，或判断一个想法是否值得继续做实验时使用。

Core Features & Use Cases

用于系统地拆分新颖性要素：objective、architecture、data or augmentation、training recipe、evaluation setting。
对每个部分评估它是“真正新”、“旧元素重组”、“调参优化”还是“当前任务设定下才算新”，并输出最终结论。
针对 AI 工作，重点关注缺少强基线、基准 cherry-pick、算力变化对新颖性的影响、以及新颖性是否主要来自规模化等问题。
输出包括：最强的 prior-work 冲突点、以 reviewer 风格提出的质疑，以及最小且有效的增强改动。
输出格式便于后续进入 paper-production-pipeline 或实验设计阶段。

Quick Start

Submit a concise description of your AI idea and any cited prior work, and run this skill to generate a formal novelty audit.

Frequently Asked Questions about research-novelty-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I evaluate the novelty of an AI research idea before submission?▼

Evaluate AI research idea novelty by systematically decomposing the objective, architecture, data augmentation, training recipe, and evaluation setting into specific novelty elements, then comparing each against prior work to identify adequate contributions and reviewer risks.

What are common reviewer concerns when assessing deep learning paper novelty?▼

Common reviewer concerns for deep learning paper novelty include missing strong baselines, benchmark cherry-picking, whether novelty stems primarily from scaling compute, and if contributions are merely hyperparameter tuning rather than genuine architectural or methodological advancements.

How do I check if my AI idea overlaps with existing prior work?▼

Check AI idea overlaps with prior work by categorizing each component as genuinely new, a recombination of old elements, parameter tuning, or novel only within the current task setting, which exposes the strongest specific conflicts and framing weaknesses.

Can I pressure test my grant proposal contributions before running experiments?▼

You can pressure test grant proposal contributions before experiments by generating a structured novelty audit that highlights reviewer-style questions, identifies prior-work conflicts, and suggests minimal framing adjustments to strengthen the core research positioning.

What is the best way to frame an AI model's contribution to avoid peer review rejection?▼

The best way to frame an AI model's contribution to avoid peer review rejection is to apply minimal effective framing adjustments based on a structured novelty audit that pre-identifies likely reviewer objections and strongest prior-work conflicts.

Does novelty auditing work for sequence representation learning ideas?▼

Novelty auditing works for sequence representation learning ideas by applying the same systematic decomposition of objectives and training recipes to expose whether the contribution is a genuine architectural shift or just an optimization tweak within current task settings.