/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

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 scripts/stata/_outputs/NN_log.smcl per the header convention) and the resulting .dta / .tex / .pdf outputs.
3. 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

1. Confirm every expected output exists in scripts/stata/_outputs/.
2. Check sessionInfo.txt was captured (package versions).
3. Run /audit-reproducibility if a manuscript exists — it now handles Stata .dta outputs via haven/pyreadstat (Pass 4.3).
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. 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

• `.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; the Monitor tool follows stderr so Claude can react to errors mid-stream.