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 ### Statistical summary and comparison ```bash python ~/.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. ### Format p-values ```bash 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. ## Workflow ### Step 1: Generate Analysis Code Structure the code with these sections: 1. `# IMPORT` — pandas, numpy, scipy, statsmodels, sklearn 2. `# LOAD DATA` — Load from original data files 3. `# DATASET PREPARATIONS` — Missing values, units, exclusion criteria 4. `# DESCRIPTIVE STATISTICS` — Summary tables if needed 5. `# PREPROCESSING` — Dummy variables, normalization 6. `# ANALYSIS` — Statistical tests per hypothesis 7. `# SAVE A...