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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Sourcelingzhi227/agent-research-skills

Added on2026-03-11

NPX Install

npx skill4agent add lingzhi227/agent-research-skills data-analysis

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:

```
# 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 pickle

Step 2: 4-Round Code Review

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

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