novelty-check

name	novelty-check
description	Verify research idea novelty against recent literature. Use when user says "novelty check", "check novelty", or wants to verify a research idea is novel before implementing.
argument-hint	["method-or-idea-description"]
allowed-tools	WebSearch, WebFetch, Grep, Read, Glob, mcp__llm-chat__chat

Novelty Check Skill

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

What Counts as Novelty

An idea qualifies as novel if it does ANY of the following:

• proposes a new method, theory, or task;
• applies an existing method to a different domain/setting that has not been systematically studied — note that applying X to Y within the same domain is NOT novel unless the application reveals surprising insights;
• gives a new explanation, mechanism, or finding for an existing problem;
• uses a new experimental setting, problem definition, or dataset.

Constants

• REVIEWER_BACKEND = llm-chat — External LLM reviewer via llm-chat MCP (model defers to LLM_MODEL env). Always ask the external reviewer for strict, high-rigor feedback.

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
   • Use 2024–2026 year filters

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 the external LLM reviewer via mcp__llm-chat__chat. Always ask the external reviewer for strict, high-rigor feedback.

Prompt 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.
• 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.

Review Tracing

After each mcp__llm-chat__chat reviewer call, save the trace following shared-references/review-tracing.md. Write files directly to .mechanist/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).