Novelty check

Skill wanshuiyin/Auto-claude-code-research-in-sleep/skills/skills-codex/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 "\u67e5\u65b0", "novelty check", "\u6709\u6ca1\u6709\u4eba\u505a\u8fc7", "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

Novelty Check Skill

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

Constants

REVIEWER_MODEL = gpt-5.6-sol — Model used via a secondary Codex agent. Must be an OpenAI model (e.g., gpt-5.6-sol, o3, gpt-4o)
REVIEWER_BACKEND = codex — Default: Codex xhigh reviewer. Use --reviewer: oracle-pro only when explicitly requested; if Oracle is unavailable, warn and fall back to Codex xhigh.

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: Fresh-Agent Verification (same-family provisional by default)

Call REVIEWER_MODEL via spawn_agent (spawn_agent) with xhigh reasoning:

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

Review Tracing

After each spawn_agent or optional oracle-pro reviewer call, save the trace following ../shared-references/review-tracing.md. Write files directly to .aris/traces/novelty-check/<date>_run<NN>/ and record searched claims, closest papers, reviewer route, raw response, and final novelty decision. Respect the --- trace: parameter when present (default: full).