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lingzhi227

Data Analysis skill

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.

$npx skills add lingzhi227/agent-research-skills --skill data-analysis
View this skill on GitHublingzhi227/agent-research-skills/skills/data-analysis/SKILL.md
Pack stars
462
Pack installs
60k
Alyn Score
49/99
Updated
9 months ago
– views – installs via Alyn

lingzhi227

Data Analysis

Data analysis Research

Install Data Analysis

Pick your agent. Every command is ready to paste.

$npx skills add lingzhi227/agent-research-skills --skill data-analysis

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads data-analysis when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the data-analysis skill”. In Codex, type $data-analysis.

The skill itself

SKILL.md

FolderGitHub

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

Tips for Data Analysis

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • This skill ships scripts. Read them before the first run, and run it in a project without secrets first.
  • • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
  • • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about Data Analysis

What does the Data Analysis skill do?

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.

How do I install Data Analysis?

Run "npx skills add lingzhi227/agent-research-skills --skill data-analysis" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does Data Analysis work in Cursor and Codex?

Yes. Data Analysis uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is Data Analysis free?

Data Analysis is open source on GitHub. It is free to install; check the license before commercial use.

Who made Data Analysis?

Data Analysis is published by lingzhi227 in the lingzhi227/agent-research-skills repository, together with 29 other skills.

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