data-analysis

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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.

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NPX Install

npx skill4agent add lingzhi227/claude-skills data-analysis

Tags

Translated version includes tags in frontmatter
statistical-analysiscode-reviewexperimental-data-analysisresearch-paperdata-processing

SKILL.md Content

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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 ADDITIONAL RESULTS
    — Extra results to pickle

Step 2: 4-Round Code Review

  1. Round 1 — Code Flaws: Mathematical/statistical errors, wrong calculations, trivial tests
  2. Round 2 — Data Handling: Missing values, units, preprocessing, test choice
  3. Round 3 — Per-Table: Sensible values, measures of uncertainty, missing data
  4. Round 4 — Cross-Table: Completeness, consistency, missing variables

Step 3: Produce Results

  • Every nominal value must have uncertainty (CI, STD, or p-value)
  • Statistical tests must be appropriate for the data type
  • Results must match actual data — never hallucinate

Allowed Packages

pandas
, 
numpy
, 
scipy
, 
statsmodels
, 
sklearn
, 
pickle

Statistical Test Selection

Data TypeTest
Two groups, normalIndependent t-test
Two groups, non-normalMann-Whitney U
Paired samplesPaired t-test / Wilcoxon
Multiple groupsANOVA / Kruskal-Wallis
CategoricalChi-square / Fisher's exact
CorrelationPearson / Spearman
RegressionOLS / Logistic / Mixed effects

Rules

  • Always report p-values for statistical tests
  • Account for relevant confounding variables
  • Use inherent package functionality (e.g., 
    formula = "y ~ a * b"
    for interactions)
  • Do not manually implement available statistical functions
  • Access dataframes using string-based column names, not integer indices

Related Skills

  • Upstream: experiment-code, experiment-design
  • Downstream: table-generation, figure-generation, backward-traceability
  • See also: math-reasoning