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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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.
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.
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.
| name | data-analysis |
| description | 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. |
| argument-hint | ["data-source"] |
Generate rigorous statistical analysis code with multi-round review.
$0 — Data source (CSV, JSON, pickle, or experiment logs)$1 — Research goal or hypothesis to test~/.claude/skills/data-analysis/references/review-prompts.mdpython ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --compare method --metric accuracy --output summary.json
python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --describe
Detects data types, recommends tests, runs comparisons, outputs effect sizes and significance stars. Requires numpy, scipy.
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --values "0.001 0.05 0.23" --format stars
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --csv results.csv --column pvalue --format latex
Formats p-values with stars, LaTeX notation, or plain text. Stdlib-only.
Structure the code with these sections:
# IMPORT — pandas, numpy, scipy, statsmodels, sklearn# LOAD DATA — Load from original data files# DATASET PREPARATIONS — Missing values, units, exclusion criteria# DESCRIPTIVE STATISTICS — Summary tables if needed# PREPROCESSING — Dummy variables, normalization# ANALYSIS — Statistical tests per hypothesis# SAVE ADDITIONAL RESULTS — Extra results to picklepandas, numpy, scipy, statsmodels, sklearn, pickle
| Data Type | Test |
|---|---|
| Two groups, normal | Independent t-test |
| Two groups, non-normal | Mann-Whitney U |
| Paired samples | Paired t-test / Wilcoxon |
| Multiple groups | ANOVA / Kruskal-Wallis |
| Categorical | Chi-square / Fisher's exact |
| Correlation | Pearson / Spearman |
| Regression | OLS / Logistic / Mixed effects |
formula = "y ~ a * b" for interactions)