| name | data-analysis |
| description | End-to-end data analysis workflow using Python and Stata, from exploration through regression to publication-ready tables and figures |
| disable-model-invocation | true |
| argument-hint | [dataset path or description of analysis goal] |
| allowed-tools | ["Read","Grep","Glob","Write","Edit","Bash","Task"] |
Data Analysis Workflow
Run an end-to-end data analysis using Python and/or Stata: load, explore, analyze, and produce publication-ready output.
Input: $ARGUMENTS -- a dataset path (e.g., data/processed/analysis_sample.dta) or a description of the analysis goal (e.g., "regress bond spreads on DC investment with state fixed effects").
Constraints
- Follow code conventions in
.claude/rules/python-stata-conventions.md
- Save Stata scripts to
code/stata/ with descriptive names
- Save Python scripts to
code/python/ with descriptive names
- Save all outputs (figures, tables, results) to
output/
- Run code-reviewer on the generated script before presenting results
- Use globals from 00_run.do for all Stata paths
Workflow Phases
Phase 1: Setup and Data Loading
- Read
.claude/rules/python-stata-conventions.md for project standards
- Read
code/stata/params.do for research parameters
- Create script with proper header (purpose, inputs, outputs)
- Load and inspect the dataset
Phase 2: Exploratory Data Analysis
Generate diagnostic outputs:
- Summary statistics: Variable types, means, missing rates
- Distributions: Histograms for key continuous variables
- Relationships: Scatter plots, correlation matrices
- Time patterns: If panel data, plot trends over time
- Group comparisons: If treatment/control, compare pre-treatment means
Save all diagnostic figures to output/figures/.
Phase 3: Main Analysis
Based on the research question:
- Stata: Use
reghdfe for panel data with high-dimensional FEs
- Python: Use
linearmodels or statsmodels for panel regression
- Standard errors: Cluster at the appropriate level (from params.do)
- Multiple specifications: Start simple, progressively add controls
- Effect sizes: Report standardized effects alongside raw coefficients
Phase 4: Publication-Ready Output
Tables (Stata):
- Use
eststo / esttab for regression tables
- Include all standard elements: coefficients, SEs, significance stars, N, R-squared
- Export as
.tex for LaTeX inclusion
Tables (Python):
- Use
stargazer or manual LaTeX formatting
- Export as
.tex and .csv
Figures:
- Python:
matplotlib + seaborn with consistent style
- Stata:
graph export as PDF
- Set explicit dimensions and 300 DPI for raster formats
- Save as PDF (vector) for manuscript inclusion
Phase 5: Save and Review
- Save all outputs to appropriate
output/ subdirectories
- Run the code-reviewer agent on the generated script
- Address any Critical or High issues from the review
Important
- Reproduce, don't guess. If the user specifies a regression, run exactly that.
- Show your work. Print summary statistics before jumping to regression.
- Check for issues. Look for multicollinearity, outliers, perfect prediction.
- Use relative paths. All paths via globals (Stata) or relative (Python).
- No hardcoded values. Use variables from params.do for sample restrictions.
