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.
How do I install this agent skill?
npx skills add https://github.com/lingzhi227/agent-research-skills --skill data-analysisIs this agent skill safe to install?
- Gen Agent Trust Hubwarn
This skill provides robust statistical analysis tools but introduces a security risk by explicitly supporting and encouraging the use of the 'pickle' format for data loading and saving, which is vulnerable to arbitrary code execution.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
- Runlayerpass
1/4 files flagged
- ZeroLeakspass
Score: 93/100 · 2 sections analyzed
What does this agent skill do?
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
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/lingzhi227/agent-research-skills/data-analysis">View data-analysis on skillZs</a>