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
- 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 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
Review Tracing
After each mcp__codex__codex or mcp__codex__codex-reply reviewer call, save the trace following shared-references/review-tracing.md. Use tools/save_trace.sh or write files directly to .aris/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).