Video-Tree-TRM5/.claude/skills/novelty-check/SKILL.md

name, description, argument-hint

name	description	argument-hint
novelty-check	Verify the novelty of research ideas. GPT cross-validation. Trigger phrases: novelty check, has anyone done this, check novelty.	method-or-idea-description

Novelty Verification

Verify novelty of: $ARGUMENTS

Goal

Perform a strict check on whether a method, idea, or experimental setting is actually new. The default stance is skepticism, not help-seeking for supporting evidence.

Working Principles

• Brutally honest: do not relax the standard just to make something look new.
• Applying X to Y is not novel by default unless the application produces an unexpected mechanism, theoretical explanation, or clearly different experimental phenomenon.
• Check the novelty of both the METHOD and the EXPERIMENTAL SETTING.
• If the method itself is not new, but the findings, conclusions, experimental setup, or failure analysis are new, state that distinction explicitly.
• Always search the last 6 months of arXiv.
• Do not rely on titles alone; read the abstract and, when necessary, the key parts of related work, intro, method, and appendix.

Workflow

Phase A: Extract Core Claims

First break the user's method description into 3-5 core technical claims. Each one should be as specific as possible.

For each claim, answer:

• What is the method?
• What problem does it solve?
• What is the key mechanism?
• What is the essential difference from an obvious baseline?

Rewrite the story-like description into searchable technical propositions and avoid vague phrasing.

Phase B: Multi-source Literature Search

Run multi-source retrieval for each claim, prioritizing recent work and similar settings.

For each claim, try at least 3 search-query sets, and make them complementary:

• Direct technical terms
• Synonyms / abbreviations / related task names
• "Problem + mechanism" combinations
• "Method + dataset / setting" combinations

Required Search Channels

1. WebSearch: arXiv / Google Scholar / Semantic Scholar / conference homepages
2. python3 .claude/tools/arxiv_fetch.py search "QUERY" --max 10
3. python3 .claude/tools/semantic_scholar_fetch.py search "QUERY" --max 10
4. python3 .claude/tools/exa_search.py search "QUERY" --max 10 (if available)
5. python3 .claude/tools/openalex_fetch.py search "QUERY" --max 10 (if available)

Search Priorities

• ICLR / NeurIPS / ICML 2025-2026
• arXiv preprints from the last 6 months
• Papers close to the method mechanism, not only papers on the same task
• Papers close to the experimental setting, not only papers using the same method

Decision Strategy

• Record potentially overlapping papers first; do not exclude them too early
• Prefer reading the abstract, intro, related work, and method sections
• If overlap looks suspicious, also read the experimental setup and appendix

Recording Requirements

For each candidate paper, record:

• Title
• Year
• Venue / status
• Relevant point
• The specific reason it may overlap
• Why it might still be a different work

If a data source is unavailable, explicitly record the fallback reason and continue with the others; do not stop the task.

Phase C: GPT Cross-Validation

Send the method description from Phase A and all candidate papers found in Phase B to /codex:rescue --fresh --wait for a second review.

The cross-validation prompt must include:

• proposed method description
• the full candidate paper list
• ask:
  • Is this method novel?
  • What is the closest prior work?
  • What is the delta?

Use high reasoning effort.

The goal of cross-validation is not to find even more papers. It is to force out the closest prior art, the smallest difference, and the risk of pseudo-novelty.

Phase D: Output Report + Wiki Integration

The output must be in English and follow a fixed structure.

Report Format

## Novelty Check Report
### Method Under Review
### Core Claims
- Claim 1: ... (novelty: high / medium / low; closest paper: ...)
- Claim 2: ... (novelty: high / medium / low; closest paper: ...)

### Recent Prior Work
| Paper | Year | Venue / Status | Overlap Point | Key Difference |
|---|---:|---|---|---|

### Overall Assessment
- score X/10
- recommendation: continue / continue cautiously / abandon
- key differentiator: ...
- positioning advice: ...

Evaluation Scale

• high: current search shows no close prior art, and the difference is concrete and technical
• medium: there is related prior work, but there is still a clear and defensible technical delta
• low: mostly a reorganization of known methods, task switching, dataset switching, hyperparameter changes, or standard engineering changes

Wiki Integration

If the project has research-wiki/, also ingest the knowledge there:

• Create a claim entity for each core claim
• Create a paper entity for each newly found paper
• Add claim-paper / paper-paper relation edges
• Rebuild query_pack

Prefer existing tools such as .claude/tools/research_wiki.py; if the wiki does not exist, skip silently and do not error.

Ingestion rules:

• Only write high-confidence information
• Claim names should be short, stable, and reusable
• Edges must include evidence; do not create empty links

Completion Criteria

Only finish when all of the following are complete:

1. 3-5 core claims have been extracted
2. Multi-source search has been completed, including the last 6 months of arXiv
3. Candidate paper abstracts have been read, and related work / method sections were read when necessary
4. GPT cross-validation has been completed
5. A structured English report has been produced
6. If research-wiki/ exists, the corresponding writes and query_pack rebuild have been completed

Failure Handling

• Missing tools: record the missing item and degrade gracefully
• Too few search results: expand synonyms, abbreviations, higher-level terms, and experimental settings
• Too many search results: prefer the most recent, most similar, and most likely overlapping work
• Conflicting evidence: read the original abstract and method sections first; do not rely on intuition