grind-lab-core/night_owl_research_agent/novelty-check

Skill: novelty-check

You verify that a research idea,$ARGUMENTS , has not already been published in substantially equivalent form.

Constants

• REVIEWER_MODEL = gpt-5.4 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.4, o3, gpt-4o). If external LLM is not configured properly, use subagent with the most powerful model instead.

Phase 1: Identify Key Claims

1. Source the idea description:

• If `output/IDEA_REPORT.md` exists (produced by the generate-idea skill): read it and extract each candidate idea's method, problem, mechanism, baselines, dataset, and spatial/temporal granularity. Run the remaining phases per idea (typically the top-ranked candidates), and aggregate into the final report.
• Otherwise, use $ARGUMENTS as the method description.
• If both are present, prefer output/IDEA_REPORT.md and treat $ARGUMENTS as a topic filter (only check ideas matching it).

1. Identify 3-5 core 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?
• What dataset does it use?
• What is the spatial and temporal granularity of the research?

Phase 2: 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

1. Known paper databases: Check against:

• ICLR 2025/2026, NeurIPS 2025, ICML 2025/2026
• Recent arXiv preprints (2025-2026)

1. Local Papers

• Also directly fetch relevant abstracts from output/paper-cache/ or paper/ if they already exist.

1. Read abstracts: For each potentially overlapping paper, WebFetch its abstract and related work section

Phase 3: Evaluate

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 2
• Ask: "Is this method novel? What is the closest prior work? What is the delta?"

If the external reviewer model is not configured correctly, use Claude Code subagent instead.

Phase 4: Novelty Report

Output a structured novelty 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]

Write report to `output/NOVELTY_REPORT.md`

Update `output/IDEA_REPORT.md` with verdict and score.

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