Novelty check

Skill wanshuiyin/Auto-claude-code-research-in-sleep/skills/skills-codex-gemini-review/novelty-check

ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.

Install

npx -y skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill novelty-check

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What its author says it does

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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.

SKILL.md

3.5 KB, as published. Nobody here has run it

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

Novelty Check Skill

Gemini overlay assurance: review_independence: cross-family and acceptance_status: accepted.

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

Constants

REVIEWER_MODEL = gemini-review — Gemini reviewer invoked through the local gemini-review MCP bridge. Set GEMINI_REVIEW_MODEL if you need a specific Gemini model override.

Instructions

Given a method description, systematically verify its novelty:

Phase A: Extract Key Claims

Read the user's method description
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:

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
Known paper databases: Check against:
- ICLR 2025/2026, NeurIPS 2025, ICML 2025/2026
- Recent arXiv preprints (2025-2026)
Read abstracts: For each potentially overlapping paper, WebFetch its abstract and related work section

Phase C: Cross-Model Verification

Call REVIEWER_MODEL via mcp__gemini-review__review_start with high-rigor review:

mcp__gemini-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__gemini-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