audit-reproducibility

name	audit-reproducibility
description	Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
argument-hint	[manuscript path] [outputs-dir] (outputs-dir defaults to scripts/R/_outputs/)
allowed-tools	["Read","Grep","Glob","Write","Bash","Task","Monitor"]
effort	high

Audit Reproducibility

Compare numeric claims in a manuscript (point estimates, standard errors, p-values, counts) against the actual outputs produced by the analysis pipeline. Report PASS / FAIL per claim against the tolerance thresholds defined in .claude/rules/replication-protocol.md.

Core principle: If the paper says ATT = -1.632 (0.584) and the code produces -1.628 (0.591), we verify — numerically — that the difference is within the documented tolerance. No more "looks close enough" eyeballing.

When to use

• Before submission. Catches the "I updated the analysis but forgot to update Table 2" bug.
• Before releasing a replication package. Verifies the code actually reproduces the paper.
• After a major revision. Ensures the paper still matches the latest code.
• Quality-gate in /commit. Pair with a pre-commit invocation on manuscript + analysis changes.

Inputs

• $0 — path to the manuscript (.tex, .qmd, .md, .pdf). Required.
• $1 — path to the outputs directory. Defaults to scripts/R/_outputs/. Recognised alternatives: scripts/stata/_outputs/ (Stata pipelines built by /stata-replication), _targets/objects/ (R targets workflows), any directory the user-specified outputs live in.

Workflow

Phase 0: Pre-flight

1. Read replication-protocol.md for the tolerance thresholds currently in effect.
2. Verify the outputs directory exists and is non-empty. If empty or stale (older than the manuscript), prompt the user to re-run their pipeline (e.g., Rscript scripts/R/00_run_all.R) before auditing.
3. Ensure a sessionInfo.txt or equivalent environment capture exists in the outputs dir.

Phase 1: Extract claims from the manuscript

Parse the manuscript for numeric claims. Patterns to match:

• Point-estimate + SE: ATT = -1.632 (0.584), $\beta = 0.342$ (0.091), hat{\tau} = 1.28** with starred significance
• Table cells: & -1.632$^{***}$ & 0.584 & in LaTeX table environments
• Counts: our sample of 2,847 firms, $N = 2{,}847$
• Summary stats: mean = 0.423, SD = 0.087
• P-values: p < 0.01, $p = 0.003$

Record each claim as a tuple:

{
  claim_id: "Table2_col3_ATT",
  location: "Table 2, Column 3, row 'Treatment'",
  kind: "point_estimate" | "standard_error" | "p_value" | "count" | "percentage",
  reported_value: -1.632,
  uncertainty: 0.584,              # only for point estimates
  significance_stars: 3,            # 0-3 or None
  raw_context: "the ATT estimate of -1.632 (0.584) indicates..."
}

Write the extracted claims to quality_reports/reproducibility_claims_[manuscript-name].json so the user can review the extraction before audit.

Phase 2: Extract results from outputs

Scan $1 for corresponding values. Priority order:

1. .rds files — readRDS(path)$coef[["treatment"]] style lookups. Can use Rscript -e "saveRDS(summary(readRDS(...)), '/tmp/audit.rds')" to extract.
2. .tex tables — parse LaTeX table cells directly; match on column headers + row labels.
3. .csv summary files — pandas/readr parse, key-value lookup.
4. .out / .log files (Stata, regress output) — regex extraction.
5. .json — direct key lookup.

Record each extracted result:

{
  source: "scripts/R/_outputs/results.rds",
  lookup_key: "fit_main$coefficients['treated']",
  value: -1.628,
  uncertainty: 0.591,
  p_value: 0.005
}

Phase 3: Match claims to results

Use fuzzy heuristics when exact labels don't match:

• Name similarity ("treatment effect" ~ "ATT" ~ "treated")
• Magnitude similarity (if two candidates have values within 10% of the reported, prefer the one with closer SE)
• Context hints from the claim's raw_context field (table number, row label, description)

For every claim, produce a match candidate with a confidence score. Claims below 0.7 confidence get flagged as "UNMATCHED — manual review needed" rather than silently passing.

Phase 4: Tolerance check

For each matched claim, apply the thresholds from replication-protocol.md:

Kind	Tolerance	Example
Integers (N, counts)	Exact	2,847 must equal 2,847
Point estimates	abs(reported - computed) < 0.01	-1.632 vs -1.628 → diff = 0.004 → PASS
Standard errors	abs(reported - computed) < 0.05	0.584 vs 0.591 → diff = 0.007 → PASS
P-values	Same significance level	p<0.01 and p<0.01 → PASS; p<0.01 and p=0.03 → FAIL
Percentages	±0.1pp	42.3% vs 42.35% → PASS

Respect any tolerance overrides the user has written into their replication-protocol.md fork (they may loosen for MC noise or tighten for administrative data).

Phase 5: Report

Write quality_reports/reproducibility_audit_[manuscript-name].md:

# Reproducibility Audit: [Manuscript Title]

**Date:** [YYYY-MM-DD]
**Manuscript:** [path]
**Outputs directory:** [path]
**Tolerance source:** .claude/rules/replication-protocol.md

## Summary

| Status | Count |
|---|---|
| PASS | N |
| FAIL (diff > tolerance) | M |
| UNMATCHED (manual review) | K |
| **Overall verdict** | **PASS / FAIL** |

## PASS (all within tolerance)
| Claim | Reported | Computed | Diff | Tolerance |
|---|---|---|---|---|
| Table2_col3_ATT | -1.632 (0.584) | -1.628 (0.591) | 0.004 / 0.007 | 0.01 / 0.05 |

## FAIL (outside tolerance — BLOCKER)
| Claim | Reported | Computed | Diff | Tolerance | Location in paper |
|---|---|---|---|---|---|

## UNMATCHED (manual review)
| Claim | Raw context | Candidate sources |
|---|---|---|

## Environment
[sessionInfo excerpt]

## Next steps
1. Fix any FAIL rows — either update the manuscript or rerun analysis.
2. Review UNMATCHED rows — add explicit lookup keys or widen the search scope.
3. After zero FAILs, the paper is replication-ready.

Exit behavior

• All PASS: exit 0, summary printed.
• Any FAIL: exit 1, summary printed to stderr. This makes the skill usable as a /commit pre-commit gate — see replication-protocol.md for the enforcement pattern.
• UNMATCHED > 0 (with 0 FAIL): exit 0 with warning — user must manually review.

Source-language coverage

The skill compares manuscript claims against outputs in three source-language ecosystems:

Source	Default outputs dir	Read-output via	Common claim sources
R (default)	scripts/R/_outputs/	readRDS(), arrow::read_parquet(), vroom::vroom()	.rds / .parquet / .csv / tinytable .tex
Stata (v1.9.0)	scripts/stata/_outputs/	haven::read_dta() from R, or pyreadstat.read_dta() from Python	.dta / esttab .tex / .smcl log values
Python	scripts/python/_outputs/ (or _targets/)	pandas.read_parquet, pickle.load	.parquet / .pickle / .csv

Stata-specific notes (v1.9.0):

• .dta outputs are read via haven::read_dta() (R), pyreadstat.read_dta() (Python), or by parsing the corresponding esttab .tex if the table-cell value is what the manuscript cites.
• Manuscript cell \input{scripts/stata/_outputs/tab_main.tex} is the strongest provenance signal — the cell value comes mechanically from the .do file. Match the location in the .tex to the regression call in 03_analyze.do.
• Clustering df adjustments can differ between reghdfe and base reg, cluster(). If a SE mismatches at the 2nd decimal, the tolerance in replication-protocol.md covers it; if it mismatches at the 1st decimal, investigate the df adjustment.

Passport-mode (v1.9.0)

When quality_reports/passports/<paper-slug>.yaml exists, the skill operates in passport mode: instead of emitting a one-shot report, it reads, updates, and rewrites the passport file in place.

• For each claims: entry in the passport, perform the same numeric audit as the default mode (extract reported value from manuscript at location, locate computed value at source_file:source_line / output_file:output_field, compare against tolerance:).
• After each claim is audited, update status in place:
  • PASS → claim within tolerance.
  • FAIL → claim outside tolerance. Record the discrepancy in the entry's notes.
  • STALE → if source_file or output_file modification time is later than last_verified_on, mark STALE and re-run the audit logic (after the rerun, status becomes PASS or FAIL — STALE is transient).
• Update last_verified_on and last_verified_by: "/audit-reproducibility" per claim.
• Update paper.last_audit at the top level.

If a claim in the manuscript is detected that has no matching passport entry, emit an UNVERIFIED warning — the author should add it (passport scope is author-curated, not auto-populated, to avoid bad inferences).

Passport mode does NOT delete passport entries. If a claim disappears from the manuscript, the passport entry remains with a STALE status — the author decides whether to delete (claim retracted) or update the entry's location (claim moved).

See .claude/rules/replication-protocol.md "Claims Provenance: passport.yaml" for the full schema and integration points (/commit, /review-paper).

Cross-references

• .claude/rules/replication-protocol.md — the tolerance contract + passport schema.
• templates/passport-template.yaml — starter file to copy for a new paper.
• .claude/skills/review-r/SKILL.md — catches code-style issues; this skill catches NUMERICAL reproducibility.
• .claude/skills/review-paper/SKILL.md — content review; pair with this skill for a full pre-submission audit.

What this skill does NOT do

• Re-run your analysis. The skill compares CURRENT outputs against manuscript claims. If the outputs are stale, re-run your pipeline first (the pre-flight phase will warn).
• Catch wrong specifications. A regression that compiles cleanly and produces a reproducible -1.632 is reproducible. Whether -1.632 is the RIGHT estimand is a review-paper / domain-reviewer question.
• Check external package versions. The sessionInfo.txt capture lets a reviewer see the env; pinning versions is on the user (via renv.lock or a DESCRIPTION file).

Long batch reruns: use the Monitor tool (Apr 2026)

When /audit-reproducibility is asked to verify all numeric claims in a paper, the safest approach is to re-run the full pipeline (00_run_all.R or equivalent) and compare the regenerated outputs to the manuscript values. For pipelines that take more than a couple of minutes, background-launch the rerun and use Anthropic's Monitor tool (Apr 2026 Week 15) to stream stdout. The audit can react to errors mid-stream rather than waiting for the entire pipeline to finish before noticing a failed step.