/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.
$ npx -y skills add OpenLAIR/dr-claw --skill aris-novelty-check --agent claude-codeHow 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.mdname: 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
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
A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power.
Repo: OpenLAIR/dr-claw
Other skills on dr-claw.
- /dr-claw
Dr. Claw skill for OpenClaw project discovery, idea intake, waiting-session triage, structured session control, event-driven notifications, and mobile reporting through the local drclaw CLI.
Open skill - /academic-researcher
Academic research assistant for literature reviews, paper analysis, and scholarly writing. Use when: reviewing academic papers, conducting literature reviews, writing research summaries, analyzing methodologies, formatting citations, or when user mentions academic research,
Open skill - /autogpt
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
Open skill - /crewai
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical
Open skill - /langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering
Open skill - /llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG
Open skill

