How to install data-analysis
npx skills add https://github.com/lingzhi227/agent-research-skills --skill data-analysisFull instructions (SKILL.md)
Source of truth, from lingzhi227/agent-research-skills.
name: data-analysis description: 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. argument-hint: [data-source]
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
Related skills
More from lingzhi227/agent-research-skills and the wider catalog.
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Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.
literature-search
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
figure-generation
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
citation-management
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
latex-formatting
Handle LaTeX formatting, templates, and styling for academic papers. Set up conference templates (ICML, ICLR, NeurIPS, AAAI, ACL), fix formatting issues, manage packages, and ensure venue-specific compliance. Use when the user needs to set up a paper template, fix LaTeX formatting, or prepare for submission.
math-reasoning
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.