| name | stata-replication |
| description | End-to-end Stata replication pipeline — scaffolds numbered `.do` files in `scripts/stata/`, executes them via the `stata-mcp` MCP server, captures logs and outputs to `scripts/stata/_outputs/`, and produces publication-ready tables (esttab) and figures (graph export). Mirrors `/data-analysis` for R-first projects. Use when user says "stata replication", "set up Stata pipeline", "scaffold the .do files", "run Stata analysis", "AEA replication package in Stata", or when a project's analysis language is Stata not R. |
| author | Claude Code Academic Workflow |
| version | 1.0.0 |
| argument-hint | [paper-or-data-pointer] [--from-r] [--no-execute] |
| disable-model-invocation | true |
| allowed-tools | ["Read","Write","Edit","Glob","Grep","Bash","Task"] |
/stata-replication — Stata pipeline scaffold + execution
Build a complete Stata replication pipeline in scripts/stata/: numbered .do files following .claude/rules/stata-code-conventions.md, executed via the stata-mcp MCP server, with outputs landing in scripts/stata/_outputs/.
When to use
- Your project's analysis language is Stata (not R). Common in econ field experiments, RCT studies, and any AEA submission where the original replication package is Stata.
- You're porting an R-first project to Stata for an AEA submission.
- You're adding a Stata robustness check to an R-first paper.
- You want a one-command reproduction:
do scripts/stata/99_run_all.do.
When NOT to use
- Your project is R-first. Use
/data-analysis.
- Your project is Python-first. Neither this skill nor
/data-analysis is the right fit; consider extending the convention rule for Python or porting one of these skills.
- You're doing quick exploratory work. The numbered-pipeline scaffold is for replication packages, not scratch notebooks.
Prerequisite: stata-mcp installed
This skill requires the stata-mcp MCP server. Install once per user:
claude mcp add stata-mcp --scope user -- uvx stata-mcp
The MCP server provides command-guarded Stata execution (refuses destructive operations like !/shell/erase), RAM monitoring, and Stata Language Server pairing. Maintained by SepineTam, 171 stars on GitHub as of 2026-05.
If stata-mcp is not installed, the skill halts at Phase 0 with installation instructions.
Workflow
Phase 0: Pre-flight
- Verify
stata-mcp is registered in the user's MCP configuration. If not → halt with install instructions.
- Verify Stata is installed locally (the MCP server cannot run without it). Output stata version to confirm.
- Confirm
scripts/stata/ directory exists or can be created.
- Read
.claude/rules/stata-code-conventions.md — every emitted .do file follows this convention.
- If
--from-r flag is set, locate the existing R pipeline at scripts/R/ and use it as a translation source. Apply the Stata → R pitfalls table from replication-protocol.md in reverse.
Phase 1: Scaffold the pipeline
Emit (or update) these files in scripts/stata/, each conforming to the header convention from stata-code-conventions.md:
scripts/stata/
├── 00_install.do # ssc install, set globals, paths, sessionInfo capture
├── 01_clean.do # raw → cleaned panel
├── 02_descriptive.do # summary tables, balance (iebaltab), attrition
├── 03_analyze.do # main regression specs (reghdfe / ivreg2 as needed)
├── 04_robustness.do # alt specs, sensitivity
├── 05_tables_figures.do # esttab .tex outputs + graph export PDFs
└── 99_run_all.do # do "01_clean.do" / do "02_..." / ...
If the paper or data source suggests specific specs (e.g., DiD with reghdfe, IV with ivreg2, RD with rdrobust), tailor 03_analyze.do accordingly.
Phase 2: Execute (unless --no-execute)
For each script in numbered order:
- Dispatch to
stata-mcp to execute the .do file.
- Capture the log (Stata writes to
scripts/stata/_outputs/NN_log.smcl per the header convention) and the resulting .dta / .tex / .pdf outputs.
- If a script fails, halt — do NOT auto-fix unless the failure is trivial (typo flagged by Stata at parse time). For substantive failures (insufficient observations, singular matrices, missing covariates), surface to the user.
For long-running scripts (> 2 minutes), use the Monitor tool to stream stdout — same pattern documented in /data-analysis and /audit-reproducibility.
Phase 3: Verify
- Confirm every expected output exists in
scripts/stata/_outputs/.
- Check
sessionInfo.txt was captured (package versions).
- Run
/audit-reproducibility if a manuscript exists — it now handles Stata .dta outputs via haven/pyreadstat (Pass 4.3).
- Report scripts run, outputs produced, any warnings from Stata.
Phase 4 (optional): R cross-check
If --from-r was set, run the R version of the same analysis (assumed to live at scripts/R/) and compare:
- Point estimates: should match to ~0.01 (per
replication-protocol.md tolerance).
- Standard errors: should match to ~0.05 (clustering df adjustments can differ slightly between Stata and R).
- Sample sizes: must match exactly.
Discrepancies are surfaced for the user to investigate — typical culprits: clustering df, default options (logit vs probit for PS), bootstrap seed handling.
Companion skills
/data-analysis — R analogue. Same pipeline shape, different language./audit-reproducibility — reads both .rds and .dta outputs. Cross-checks manuscript claims against the produced values. Updated in v1.9.0 to handle Stata outputs./review-paper — if the paper exists and cites tables/figures produced by this pipeline, /review-paper auto-invokes /audit-reproducibility (per cross-artifact-review.md).
Anti-patterns
- Hand-editing
.dta files. Never. All transformations happen via the .do files; .dta outputs are derived and reproducible.
- Skipping the
99_run_all.do. This is the AEA-mandated one-command entry point. Build it even for small projects.
- Using
, robust by default. Use , cluster(id) at the appropriate level — see stata-code-conventions.md §6.
- Hand-formatting tables in LaTeX. Use
esttab and \input{} — see stata-code-conventions.md §4.
- Pinning Stata version in only one .do file. Every
.do file starts with version 18 per the convention.
Cross-references
Long-running fits / batch reruns: use the Monitor tool (Apr 2026)
Long Stata fits (multi-hour bootstrap with cluster bootstrap, large reghdfe with millions of observations, simulation studies) should be background-launched and tailed with the Monitor tool — same pattern as /data-analysis and /audit-reproducibility for R / Python. The .do file logs to SMCL; the Monitor tool follows stderr so Claude can react to errors mid-stream.
