Data Analysis

by lingzhi2279e6c085d65e3No license386 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 7 months ago

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.

Includes scriptsData & Analytics
AI-generated overview

Generates reviewed statistical analysis code and reports with p-values, effect sizes and confidence intervals.

What it does
Generates statistical analysis code for experimental data, structured into import, load, preparation, descriptive, preprocessing, analysis and results sections. It selects appropriate tests, runs a four-round code review covering code flaws, data handling, per-table and cross-table checks, and produces reports with p-values, effect sizes and confidence intervals. Two bundled scripts produce statistical summaries and comparisons, and format p-values with stars, LaTeX or plain text.
When to use it
Use it when analyzing experimental data for a paper and you need statistically rigorous results with uncertainty reported. It suits comparisons between groups, correlations and regression on CSV, JSON, pickle or experiment log inputs.
Requirements
Python with numpy, scipy, pandas, statsmodels, sklearn and pickle; the skill ships two executable scripts (stat_summary.py and format_pvalue.py) and a review-prompts reference file.

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.jsonpython ~/.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 starspython ~/.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

Source and attribution

Source:lingzhi227/agent-research-skillsinskills/data-analysisat commit9e6c085

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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