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Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction.
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SOC 職業分類に基づく
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
Explore and analyze GitHub repositories related to a research topic. Reads deep-research output, discovers repos from multiple sources, deeply analyzes code, and produces integration blueprints.
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Write ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use when implementing experiments for a research paper.
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
| name | novelty-assessment |
| description | Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction. |
| argument-hint | ["idea"] |
Rigorously assess whether a research idea is novel through systematic literature search.
$0 — Research idea description, title, or JSON filepython ~/.claude/skills/idea-generation/scripts/novelty_check.py \
--idea "Your research idea description" \
--max-rounds 10 --output novelty_report.json
python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
--query "relevant search query" --max-results 10
~/.claude/skills/novelty-assessment/references/assessment-prompts.mdFor each round:
If novel, identify:
Be a harsh critic for novelty. Ensure there is a sufficient contribution
for a new conference or workshop paper. A trivial extension of existing
work is NOT novel. The idea must offer a meaningfully different approach,
formulation, or insight.
{
"decision": "novel" | "not_novel",
"confidence": "high" | "medium" | "low",
"justification": "After searching X rounds...",
"most_similar_papers": [
{"title": "...", "year": 2024, "overlap": "..."}
],
"differentiation": "Our idea differs because..."
}