# 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. - Skill: `yananlong/research-novelty-review` (Agent Skill, multi-file: 15 files) - Install (CLI): `npx skillmds@latest add yananlong/research-novelty-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/yananlong/research-novelty-review/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: yananlong (https://skillmd.com/u/yananlong) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/yananlong/research-novelty-review --- # 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. When reviewing a paper, consume `research-paper-review` and the exact validated `research-systematic-literature-review` pack before deciding. 6. Keep novelty positioning separate from empirical validity: a strong novelty rating does not show that the method works or authorize evidence promotion. 7. 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. - If the user asks for project-level sequencing, current-state inspection, or coordination across multiple research stages, invoke `research-pipeline-planner` first instead of treating novelty review as the whole task. - 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. - Preserve whether the proposed contribution was formulated before or after inspecting task-relevant outcomes; novelty review must not erase outcome-informed selection. ### Paper-review integrated mode - Use this mode when `research-paper-review` asks for novelty, impact, or contribution-positioning context for a concrete paper. - Consume: - `/summary.md` - `/context/literature-context.md` when present - `./literature-review/` artifacts when present - the paper's cited closest prior work from `summary.md` or the full text - Do not re-run broad literature discovery if a fresh paper-context evidence map already exists and is adequate. Instead, search only the remaining kill-shot gaps. - Output a bottom-line `novelty-context.md` or copy the key decision into `/context/novelty-context.md` for the paper-review bundle. - Rate novelty and impact separately: a paper can be useful or important while still not being novel. ## Relationship to sibling skills - `research-paper-review` owns first-pass technical critique of one paper. This skill should be invoked from paper review only for novelty, contribution positioning, impact framing, or prior-art pressure testing. - `research-idea-discovery` owns the front-of-funnel task of generating and ranking candidate research ideas from a broad direction. Use it before this skill when the user does not yet have a concrete idea, method, protocol, artifact, or finding to compare against prior art. - `research-systematic-literature-review` owns broader evidence discovery. Use its paper-context evidence map before this skill when novelty or impact claims depend on external literature coverage. - `research-zotero` supplies curated prior-art seeds when the user has a relevant library or collection. - `research-review-loop` can track whether novelty/positioning issues have been resolved across revisions. - `research-paper-plan` can use the narrowest defensible positioning from this skill as the contribution statement for a manuscript. - `research-experiment-plan` must independently establish the decision contract and evidential route; do not treat a novelty decision as an experiment gate. ## 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 `research-paper-review` summary and context artifacts - existing Zotero library artifacts - selection history and current evidence class ## Hard stops - Stop if the proposed contribution is too vague to search. - If the user wants to find or generate ideas rather than evaluate a concrete one, route to `research-idea-discovery`. - 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. - Do not change cases, comparators, contribution boundaries, or search interpretation merely to preserve a desired novelty rating after unfavorable evidence appears. - Do not describe novelty as independently established unless a materially separate review pass actually occurred; a separate role label in the same context is self-review. - Do not use novelty pressure to convert an outcome-informed claim into a confirmatory one. Preserve the exploratory status and design a separate confirmatory evaluation downstream. ## 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, the exact narrow positioning that survives the review, selection-history caveats, and whether the review was self-review or materially independent. In linked mode it also binds the exact seven-file literature pack with per-file SHA-256 digests, corpus identity, assurance verdict, and unresolved high-priority novelty-critical question IDs. - In paper-review integrated mode, `novelty-decision.json` should also capture: - `impact_positioning_rating` (1-5) - `literature_context_used` - `paper_review_summary_used` - `claims_to_qualify` - `missing_prior_work` - `review_findings_to_add` ## 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. - Record which components were specified before versus after inspection of relevant outcomes. ### 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. - If a `research-systematic-literature-review` paper-context evidence map exists, use it as the first prior-art source and search only for obvious missing overlaps or decision-critical gaps. - 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. - Keep negative and contradictory evidence visible even when a narrow positioning survives. ### 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 - Do not issue rating 4 or 5 from linked evidence unless the exact literature pack validates with an adequate verdict and has no unresolved high-priority novelty-critical question. - Rating 3 under material literature uncertainty requires explicit narrow positioning, a change condition, and claims to qualify. - State explicitly that the rating governs positioning only. It does not certify empirical correctness, confirmatory status, implementation validity, or route readiness. - 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. Record the actual independence dimensions; otherwise call it self-review. - When handing off to experiment planning, include current evidence class, outcome-informed selection history, unresolved novelty threats, and predecessor failures. Use `../research-pipeline-planner/references/epistemic-assurance-contract.md` for any requested promotion. ### 6) Feed findings back to paper review when integrated - In paper-review integrated mode, write a short handoff section with: - issue title to add to `final_issues.json` - exact paper quote or location - explanation grounded in the prior-art matrix - suggested `comment_type` - suggested `impact_rating` - suggested `confidence_rating` - Use `claim_accuracy` for overstated novelty or significance. - Use `missing_information` for omitted closest prior work or absent benchmark context. - Use `presentation` for framing issues that do not materially affect a core claim. - Use `methodology` when the novelty/impact claim depends on an evaluation protocol that is not competitive with field norms. ## 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` - `references/paper-review-integration.md` - `references/literature-assurance-contract.md` - `../research-pipeline-planner/references/epistemic-assurance-contract.md` ## Scripts - `scripts/init_novelty_pack.py`: create `novelty-report.md`, `prior-art-matrix.md`, `search-log.md`, and schema-versioned `novelty-decision.json` in a standalone directory or the suite's `novelty-review/` directory. - `scripts/validate_novelty_pack.py`: validate report/JSON reciprocity and, in linked mode, rerun the exact literature pack before enforcing rating gates.