/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 output/.

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.

If stata-mcp is not installed, the skill halts at Phase 0 with installation instructions.

Workflow

Phase 0: Pre-flight

1. Verify stata-mcp is registered in the user's MCP configuration. If not → halt with install instructions.
2. Verify Stata is installed locally (the MCP server cannot run without it). Output stata version to confirm.
3. Confirm scripts/stata/ directory exists or can be created.
4. Read .claude/rules/stata-code-conventions.md — every emitted .do file follows this convention.
5. 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:

1. Dispatch to stata-mcp to execute the .do file.
2. Capture the log (Stata writes to output/NN_log.smcl per the header convention) and the resulting .dta / .tex / .pdf outputs.
3. If a script fails, first append its specification to the ledger (step 4) with Status failed and the error in Why, then 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.
4. Append every specification each estimation .do file ran — kept, dropped, or failed — to quality_reports/spec-ledger.md, with the same columns, commit stamp and append-only block as /data-analysis Phase 3 ("Specification ledger"). A failed run is a row too, with Status failed and the error in Why.

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

1. Confirm every expected output exists in output/.
2. Check output/sessionInfo_stata.txt was captured (package versions).
3. Run /audit-reproducibility if a manuscript exists — it reads Stata .dta outputs via haven/pyreadstat.
4. 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.
• /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

• .claude/rules/stata-code-conventions.md — the discipline contract.
• .claude/rules/replication-protocol.md — tolerance thresholds (applies across R / Stata / Python).
• stata-mcp on GitHub — the MCP server this skill depends on.
• AEA Data Editor checklist — replication-package standards.

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 (output/NN_log.smcl). Monitor does not attach to a background job or its stderr: only the stdout of the command you give it becomes events. So run Monitor on a command that tails the log and filters for progress lines and Stata errors, e.g. tail -f output/NN_log.smcl | grep --line-buffered -E '\{err\}|r\([0-9]+\);|<your progress marker>', so Claude can react to errors mid-stream (a multi-hour run needs persistent: true, then TaskStop once the job ends).