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/aris-novelty-check

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.

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dr-claw
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Install
$ npx -y skills add OpenLAIR/dr-claw --skill aris-novelty-check --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/aris-novelty-check

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

aris-novelty-check.SKILL.md
name: aris-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.
argument-hint: "[method-or-idea-description]"
allowed-tools: WebSearch, WebFetch, Grep, Read, Glob, mcp__codex__codex
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

Novelty Check Skill

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

Constants

  • REVIEWER_MODEL = `gpt-5.4` — Model used via Codex MCP. Must be an OpenAI model (e.g., `gpt-5.4`, `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:

config: {"model_reasoning_effort": "xhigh"}

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
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