SkillsAdd

lingzhi227/agent-research-skills

data-analysis

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.

All-time #6929 Trending #8190 First seen Feb 20, 2026
8-week activity · all time api

Installation

$ npx skills add lingzhi227/agent-research-skills --skill data-analysis
View on skills.sh ↗ GitHub repository ↗

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 317
Default branch main
Open issues 3
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,571 B
  • docs SUMMARY.md 267 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,723 installs

SKILL.md

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:

  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 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-code/), [experiment-design](../experiment-design/)
  • Downstream: [table-generation](../table-generation/), [figure-generation](../figure-generation/), [backward-traceability](../backward-traceability/)
  • See also: [math-reasoning](../math-reasoning/)