Skip to content
Automation
Skill

/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.

From plugin
dr-claw
1.1k174 skills
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
Read more
Ships withdr-claw

A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power.

Get the whole plugin
Stats
1,091
Stars
119
Forks
Active
Maintenance
JavaScript
Language
6d ago
Last commit
6mo ago
Created

Repo: OpenLAIR/dr-claw

Other skills on dr-claw.