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.
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.
/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.
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.
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.
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.
