Data analysis

Skill kenantang/codex-and-claude-skills/collected-academic-research-skills/sources/lingzhi227__agent-research-skills/skills/data-analysis

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Install

npx -y skills add kenantang/codex-and-claude-skills --skill data-analysis

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What its author says it does

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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.

SKILL.md

3.5 KB, 777 tokens by cl100k_base, as published. Nobody here has run it

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

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

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

Gives 0 of the 12 instructions most data analysis skills give in 777 tokens

Counted across 286 of the 286 authors here whose files we hold, read 2026-08-06

use excel formulas instead of hardcoded calculated valuesin 35 of 286, across 7 files
match existing template conventions when modifying filesin 35 of 286, across 7 files
document sources for all hardcoded valuesin 35 of 286, across 7 files
write minimal concise python codein 35 of 286, across 7 files
place all assumptions in separate assumption cellsin 32 of 286, across 5 files
apply industry-standard color coding to financial modelsin 31 of 286, across 5 files
format years as text stringsin 30 of 286, across 3 files
recalculate formulas using recalc.py after modificationsin 30 of 286, across 3 files
format negative numbers using parenthesesin 30 of 286, across 3 files
fix all identified formula errors before finishingin 27 of 286, across 1 file
use colorblind-safe palettesin 19 of 286, across 12 files
Name tests after the prevented bugin 13 of 286, across 8 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.