novelty-check

name	novelty-check
description	Validates that a research idea is genuinely novel vs. existing literature. Searches ArXiv, Semantic Scholar, and WebSearch for near-duplicate work. Produces a novelty verdict and evidence. Run on each idea from idea-discovery-pipeline before investing in experiments.
argument-hint	["method-or-idea-description"]
tools	Bash, WebFetch, WebSearch, Read, Write, Grep, Glob

Skill: novelty-check

You verify that a research idea,$ARGUMENTS , has not already been published in substantially equivalent form.

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Constants

• REVIEWER_MODEL = gpt-5.4 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.4, o3, gpt-4o). If external LLM is not configured properly, use subagent with the most powerful model instead.

Phase 1: Identify Key Claims

1. Source the idea description:
   • If output/IDEA_REPORT.md exists (produced by the generate-idea skill): read it and extract each candidate idea's method, problem, mechanism, baselines, dataset, and spatial/temporal granularity. Run the remaining phases per idea (typically the top-ranked candidates), and aggregate into the final report.
   • Otherwise, use $ARGUMENTS as the method description.
   • If both are present, prefer output/IDEA_REPORT.md and treat $ARGUMENTS as a topic filter (only check ideas matching it).
2. Identify 3-5 core 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?
   • What dataset does it use?
   • What is the spatial and temporal granularity of the research?

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Phase 2: 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. Local Papers

   • Also directly fetch relevant abstracts from output/paper-cache/ or paper/ if they already exist.

4. Read abstracts: For each potentially overlapping paper, WebFetch its abstract and related work section

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Phase 3: Evaluate

Call REVIEWER_MODEL via Codex MCP (mcp__codex__codex) with xhigh reasoning:

config: {"model_reasoning_effort": "xhigh"}

Prompt should include:

• The proposed method description
• All papers found in Phase 2
• Ask: "Is this method novel? What is the closest prior work? What is the delta?"

If the external reviewer model is not configured correctly, use Claude Code subagent instead.

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Phase 4: Novelty Report

Output a structured novelty 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: [what a reviewer would cite as prior work]

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

Write report to output/NOVELTY_REPORT.md

Update output/IDEA_REPORT.md with verdict and score.

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