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Research novelty review

Skill kenantang/codex-and-claude-skills/collected-academic-research-skills/sources/yananlong__codex-skills/research/research-novelty-review

Run a stringent, adversarial novelty review over a concrete research idea, method, protocol, artifact, or claimed finding. Use when asked to assess novelty, position a paper or project, build a prior-art matrix, decide whether something is incremental, or pressure-test whether the right move is to proceed, reframe, or abandon. Prefer `research-paper-review` for first-pass technical critique of a single paper artifact and `research-rebuttal` when the task is to answer concrete reviewer comments rather than establish novelty positioning from scratch.From its SKILL.md

Install
npx -y skills add kenantang/codex-and-claude-skills --skill research-novelty-review

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SKILL.md

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Research Novelty Review

Quick start

  1. Start skeptical: treat the contribution as incremental until the evidence says otherwise.
  2. Decompose the claim into searchable units before looking at any literature.
  3. Search strongest overlaps first, not flattering long-tail analogies.
  4. Write down novelty-killing objections explicitly before giving any green light.
  5. End with a 1-5 novelty decision rating, not a coarse ternary label, plus the narrowest defensible positioning.

Modes

Standalone mode

  • Work from the user prompt plus any local notes, citations, or existing review artifacts.
  • Do not require a suite root.
  • Remain collaboration-friendly: if literature review or review-loop artifacts already exist, consume them; if broader retrieval or a second adversarial pass would materially help, recommend or invoke that workflow rather than staying artificially isolated.

Orchestrated mode

  • Prefer the canonical directory ./novelty-review/.
  • Read upstream context from research-brief.md, artifact-index.md, ./literature-review/, and ./zotero/ when present.
  • Keep outputs legible to downstream experiment planning and paper planning.

Input contract

  • Minimum: a concrete method, protocol, artifact, finding, or contribution claim.
  • Prefer:
    • target venue or community
    • closest known prior work
    • scope of novelty under consideration
    • existing literature or review artifacts
    • existing Zotero library artifacts

Hard stops

  • Stop if the proposed contribution is too vague to search.
  • Stop if browsing or a usable corpus is unavailable.
  • Stop if the claim bundles so many moving parts that a fair novelty comparison is impossible.
  • In standalone mode, do not force suite initialization when a direct adversarial review will do.

Output contract

  • Primary files:
    • novelty-report.md
    • prior-art-matrix.md
    • search-log.md
    • novelty-decision.json
  • In orchestrated mode, these live under ./novelty-review/.
  • In standalone mode, any target directory is valid.
  • novelty-decision.json should capture the final 1-5 novelty rating, confidence rating, and the exact narrow positioning that survives the review.

Workflow

1) Decompose the claim before searching

  • Write the contribution in one sentence.
  • Split novelty into:
    • framing
    • method
    • evaluation protocol
    • artifact or dataset
    • empirical finding
  • Reject blended claims that hide which part is allegedly new.

2) Search with a kill-shot mindset

  • Use references/adversarial-query-patterns.md and references/search-log-template.md.
  • If the user maintains a relevant Zotero library or collection, invoke research-zotero first or consume ./zotero/zotero-items.json as a curated seed corpus.
  • Search the strongest plausible overlaps first.
  • Prioritize recent literature and venue-appropriate sources before padding with weaker analogies.
  • Log exact queries, filters, and what each search was trying to falsify.

3) Build the prior-art matrix

  • Use references/prior-art-matrix-template.md.
  • Record closest overlaps, not just vaguely related work.
  • Score how much each prior work threatens the novelty claim on a 1-5 scale.
  • Prefer concrete overlap dimensions over narrative hand-waving.

4) Write the objections first

  • Use references/reviewer-objection-rubric.md and references/overlap-scoring-rubric.md.
  • State the strongest objections a skeptical reviewer would raise.
  • Flag renaming, recombination, routine scaling, and standard-bundle effects aggressively.
  • If the novelty is weak but the usefulness is real, say so clearly.
  • Keep the ratings explicit:
    • overlap threat rating: 1-5
    • surviving novelty strength: 1-5
    • confidence in the decision: 1-5

5) Decide and position narrowly

  • Use references/decision-scale.md.
  • End with:
    • novelty_decision_rating on a 1-5 scale
    • decision_confidence_rating on a 1-5 scale
  • Offer the narrowest defensible positioning that survives the objections.
  • Use this interpretation:
    • 1: abandon; the core claim is effectively killed by overlap
    • 2: major reframe required before proceeding
    • 3: narrow proceed only; some novelty survives but only under a sharply reduced claim
    • 4: proceed with careful positioning; novelty looks real but vulnerable
    • 5: strong novelty position; closest overlaps do not materially undercut the core claim
  • If deeper retrieval would materially improve confidence, collaborate with research-systematic-literature-review.
  • If a second adversarial pass would help and delegation is explicitly available and permitted, an independent review pass is allowed. Do not assume that permission.

References

  • references/novelty-checklist.md
  • references/prior-art-matrix-template.md
  • references/search-log-template.md
  • references/adversarial-query-patterns.md
  • references/decision-scale.md
  • references/overlap-scoring-rubric.md
  • references/reviewer-objection-rubric.md
  • references/tabmol-ddi-ood-adapter.md

Script

  • scripts/init_novelty_pack.py: create novelty-report.md, prior-art-matrix.md, search-log.md, and novelty-decision.json in a standalone directory or the suite's novelty-review/ directory.

What ships with it: 10 files

6.3 KB alongside SKILL.md, 1 of them executable

agents/

scripts/

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