novelty-check

name	novelty-check
description	Verify research idea novelty against recent literature. Use when user says "查新", "novelty check", "有没有人做过", "check novelty", or wants to verify a research idea is novel before implementing.

Override for Codex users who want Claude Code, not a second Codex agent, to act as the reviewer. Install this package after skills/skills-codex/*.

Prerequisites

• Install the base Codex-native skills first: copy skills/skills-codex/* into ~/.codex/skills/.
• Then install this overlay package: copy skills/skills-codex-claude-review/* into ~/.codex/skills/ and allow it to overwrite the same skill names.
• Register the local reviewer bridge:

  codex mcp add claude-review -- python3 ~/.codex/mcp-servers/claude-review/server.py
  

• This gives Codex access to mcp__claude-review__review_start, mcp__claude-review__review_reply_start, and mcp__claude-review__review_status.

Novelty Check Skill

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

Constants

• REVIEWER_MODEL = claude-review — Claude reviewer invoked through the local claude-review MCP bridge. Set CLAUDE_REVIEW_MODEL if you need a specific Claude model override.

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 mcp__claude-review__review_start with high-rigor review:

mcp__claude-review__review_start:
  prompt: |
    [Full novelty briefing + prior work list + specific novelty questions]

After this start call, immediately save the returned jobId and poll mcp__claude-review__review_status with a bounded waitSeconds until done=true. Treat the completed status payload's response as the reviewer output, and save the completed threadId for any follow-up round. 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
• "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