idea-generation
Idea Generation
Generate and refine novel research ideas with literature-backed novelty assessment.
Input
$0— Research area, task description, or existing codebase context$1— Optional: additional context (e.g., "for NeurIPS", constraints)
Scripts
Novelty check against Semantic Scholar
python ~/.claude/skills/idea-generation/scripts/novelty_check.py \
--idea "Adaptive attention head pruning via gradient-guided importance" \
--max-rounds 5
Performs iterative literature search to assess if an idea is novel.
More from lingzhi227/claude-skills
literature-search
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
24data-analysis
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.
18paper-revision
Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.
17deep-research
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.
16rebuttal-writing
Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.
14algorithm-design
Design algorithms with LaTeX pseudocode and UML diagrams. Generate algorithmic environments, Mermaid class/sequence diagrams, and ensure consistency between pseudocode and implementation. Use when formalizing methods for a paper.
13