data-analysis
Data Analysis
Generate rigorous statistical analysis code with multi-round review.
Input
$0— Data source (CSV, JSON, pickle, or experiment logs)$1— Research goal or hypothesis to test
References
- 4-round code review prompts:
~/.claude/skills/data-analysis/references/review-prompts.md
Scripts
More from lingzhi227/claude-research-skills
math-reasoning
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
1paper-writing-section
Write a specific section of an academic paper (Abstract, Introduction, Background, Related Work, Methods, Experiments, Results, Discussion/Conclusion) with section-specific guidance and two-pass refinement. Use when the user wants to write, draft, or improve a paper section.
1paper-to-code
Convert an ML research paper into a complete, runnable code repository. 3-stage pipeline from Paper2Code — Planning (UML + dependency graph) → Analysis (per-file logic) → Coding (dependency-ordered generation). Use for reproducing paper methods.
1experiment-code
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.
1algorithm-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.
1idea-generation
Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty.
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