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yingaowang-casia/shushu-novelty-finder

Analysis updated 2026-07-15 · repo last pushed 2026-06-06

10PythonAudience · researcherComplexity · 2/5MaintainedSetup · moderate

TLDR

A tool that maps existing research papers in a field, identifies gaps, and proposes novel research ideas with experiments and potential reviewer objections. Runs as a skill inside OpenAI Codex.

Mindmap

mindmap
  root((repo))
    What it does
      Maps existing literature
      Finds research gaps
      Generates ranked ideas
    Modes
      Literature Lineage mode
      Idea Generation mode
    Key features
      Closest existing work
      Minimum experiments
      Reviewer objections
      Kill criteria checklist
    Tech stack
      Python
      OpenAI Codex skill
    Audience
      Graduate students
      Academic researchers
    Values
      Intellectual honesty
      Tags unverified claims
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Code map

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What do people build with it?

USE CASE 1

Map the landscape of existing papers in a research area to see what's saturated and what gaps remain.

USE CASE 2

Generate ranked novel research ideas from a direction or seed paper, each with experiments and baselines.

USE CASE 3

Stress-test a research idea by seeing the closest competing work and anticipated reviewer objections before committing.

USE CASE 4

Check whether a paper idea is already taken by surveying related literature with evidence.

What is it built with?

PythonOpenAI Codex

How does it compare?

yingaowang-casia/shushu-novelty-finderalsgur9865-sketch/second-brain-enginecompumaxx/gba-video-studio
Stars101010
LanguagePythonPythonPython
Last pushed2026-06-06
MaintenanceMaintained
Setup difficultymoderatemoderatehard
Complexity2/53/54/5
Audienceresearcherdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires an OpenAI Codex environment and an OpenAI API key to install and trigger the skill.

No license information is provided in the repository, so default copyright restrictions apply and usage rights are unclear.

In plain English

Shushu Novelty Finder is a tool for researchers who want to figure out what's genuinely new in their field before committing to a project. Instead of just brainstorming ideas that sound good, it walks you through the existing literature in a specific research area, identifies what's already been done, and then proposes novel ideas that actually fill a gap. It's designed as a "skill" for OpenAI's Codex, meaning you install it and then trigger it by typing a command in your Codex chat. The tool works in two main modes. The first is Literature Lineage mode, where you give it a research direction and it maps out the landscape: grouping similar papers, explaining what each key paper actually contributed, and flagging which areas are already saturated versus which gaps still have room for new work. The second is Idea Generation mode, where you provide either a direction or a "seed paper" and it outputs ranked research ideas. These aren't just one-liners, each idea comes with the closest existing work it would compete against, a proposed minimum experiment, suggested baselines, anticipated reviewer objections, and clear criteria for what results would kill or support the idea. The people who'd use this are graduate students, academic researchers, or anyone trying to write a paper and wondering if their idea is already taken. For example, if you're working on combining retrieval-augmented generation with large language models for chemistry, you could ask the tool to first map out who's done what in that space, then generate three to five concrete ideas that haven't been explored yet, each with a reality check on whether a reviewer would buy it. What's notable is the emphasis on intellectual honesty. The tool explicitly avoids saying "nobody has done this" without evidence, tags unverified claims, and includes a built-in checklist to make sure its output actually holds up. It pushes you to think about kill criteria, what would make you abandon an idea, before you sink time into experiments.

Copy-paste prompts

Prompt 1
I want to explore research gaps in retrieval-augmented generation for large language models in chemistry. Use the Shushu Novelty Finder skill to map the literature landscape and then generate 3-5 novel ideas with proposed experiments and kill criteria.
Prompt 2
Here is a seed paper on multimodal learning. Use the Shushu Novelty Finder skill to find the closest existing competing work, anticipate reviewer objections, and suggest what minimum experiment would validate a novel extension.
Prompt 3
Before I commit to writing a paper on efficient fine-tuning methods, use the Shushu Novelty Finder skill to survey what has already been done in this space and flag which areas are saturated versus which still have room for new work.
Prompt 4
Use the Shushu Novelty Finder skill to generate research ideas in graph neural networks for drug discovery, and for each idea include suggested baselines and criteria that would kill or support the idea.

Frequently asked questions

What is shushu-novelty-finder?

A tool that maps existing research papers in a field, identifies gaps, and proposes novel research ideas with experiments and potential reviewer objections. Runs as a skill inside OpenAI Codex.

What language is shushu-novelty-finder written in?

Mainly Python. The stack also includes Python, OpenAI Codex.

Is shushu-novelty-finder actively maintained?

Maintained — commit in last 6 months (last push 2026-06-06).

What license does shushu-novelty-finder use?

No license information is provided in the repository, so default copyright restrictions apply and usage rights are unclear.

How hard is shushu-novelty-finder to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is shushu-novelty-finder for?

Mainly researcher.

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