agentleFS
ImmortalqxImmortalqx/my_codex_skills/novelty-check/SKILL.md

novelty-check

Skill54 starsChanged 5 months agoMITView on GitHub

Verify research idea novelty against recent literature. Use when user says \"查新\", \"novelty check\", \"有没有人做过\", \"check novelty\", or wants to verify a research idea is novel before implementing.

12 other projects use a version of this, 10 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use research-refine, research-refine-pipeline and 4 more.

Other files people use alongside this one.

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.
  • raja21068Skill

    research-refine

    Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.4 review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.

    Also in 4 projects that use this

  • raja21068Skill

    research-refine-pipeline

    Run an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.

    Also in 3 projects that use this

  • +4 more from raja21068

The file

We split this skill into the rules it gives an agent

9 rules. 8 are common, 1 is distinctive, and 0 are very specific to this project.

  • 8

    Common

    Most of the 11 edited versions we read keep these.

  • 1

    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:

    Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS

    line 8
  2. Common:

    Read the user's method description

    line 16
  3. Common:

    Search arXiv, Google Scholar, Semantic Scholar

    line 28
  4. Common:

    Use specific technical terms from the claim

    line 29
  5. Common:

    Known paper databases: Check against:

    line 33
  6. Common:

    Check both the method AND the experimental setting for novelty

    line 83
  7. Common:

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

    line 84
  8. Common:

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

    line 85
  9. Distinctive:

    Treat unavailable sources as uncertainty, not as evidence of novelty

    line 86

“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 Skill

Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS

Instructions

Given a method description, systematically verify its novelty:

Phase A: Extract Key Claims

  1. Read the user's method description
  2. 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?

Phase B: Multi-Source Literature Search

For EACH core claim, search using ALL available sources:

  1. Web Search (via WebSearch):
    • Search arXiv, Google Scholar, Semantic Scholar
    • Use specific technical terms from the claim
    • Try at least 3 different query formulations per claim
    • Include year filters for 2024-2026
  2. Known paper databases: Check against:
    • ICLR 2025/2026, NeurIPS 2025, ICML 2025/2026
    • Recent arXiv preprints (2025-2026)
  3. Read abstracts: For each potentially overlapping paper, WebFetch its abstract and related work section

Phase C: Evidence Synthesis

Compare the proposed idea against the strongest overlapping work found in Phase B. Base the comparison directly on the collected evidence.

For each close paper, identify:

  • Which claim(s) it overlaps
  • Whether the overlap is in the method, problem setting, objective, data, or empirical finding
  • Whether the proposed difference is technical, experimental, or only a framing difference
  • What evidence supports the difference: abstract, method section, figures, experiments, or claims

Phase D: Novelty Report

Output a structured report:

## Novelty Check Report

### Proposed Method
[1-2 sentence description]

### Core Claims
1. [Claim 1] — Novelty: HIGH/MEDIUM/LOW — Closest: [paper]
2. [Claim 2] — Novelty: HIGH/MEDIUM/LOW — Closest: [paper]
...

### Closest Prior Work
| Paper | Year | Venue | Overlap | Key Difference |
|-------|------|-------|---------|----------------|

### Overall Novelty Assessment
- Score: X/10
- Recommendation: PROCEED / PROCEED WITH CAUTION / ABANDON
- Key differentiator: [what makes this unique, if anything]
- Risk: [closest prior-work threat]

### Suggested Positioning
[How to frame the contribution to maximize novelty perception]

Important 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
  • Always check the most recent 6 months of arXiv — the field moves fast
  • Treat unavailable sources as uncertainty, not as evidence of novelty
  • Base the final judgment on evidence and explicitly state uncertainty

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.