wanshuiyin/novelty-check

Novelty Check Skill

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

Constants

• REVIEWER_MODEL = gpt-5.5 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.5, o3, gpt-4o)

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: Cross-Model Verification

Call REVIEWER_MODEL via Codex MCP (mcp__codex__codex) with xhigh reasoning. When the method description plus the Phase-B paper list is more than a short note, avoid pasting it inline into the MCP prompt. Write a dossier file such as NOVELTY_DOSSIER.md (or a project-local equivalent) containing the method description, core claims, candidate papers, and the exact questions below, then send only the file path:

mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    Read the novelty dossier at <absolute path to NOVELTY_DOSSIER.md> and
    follow all instructions in it.

Dossier contents should include:

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

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: [what a reviewer would cite as prior work]

### 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
• Anti-hallucination for Closest Prior Work. Every paper in the prior-work table must pass pre-search verification via verify_papers.py (canonical name resolved per shared-references/integration-contract.md §2; 3-layer arXiv / CrossRef / Semantic Scholar fallback inside the helper itself). Policy D1 (primary + degraded-output fallback): if the helper is unresolved or its invocation fails, tag candidate entries [UNVERIFIED] and surface the uncertainty rather than dropping them. Never fabricate arXiv IDs, DOIs, or titles from memory. Full protocol in shared-references/citation-discipline.md § Pre-Search Verification Protocol.

Review Tracing

After each mcp__codex__codex or mcp__codex__codex-reply reviewer call, save the trace following shared-references/review-tracing.md (Policy C — forensic; never silently skip). Use save_trace.sh (resolved per the chain in shared-references/integration-contract.md §2) or write files directly to .aris/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).