Validate the replication package for the sewage-house-prices project. Runs 10 checks covering script execution, file integrity, output freshness, dependency verification, data provenance, and README completeness (AEA format). This skill should be used when asked to "audit replication", "check the package", or "verify reproducibility".
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Use audit-replication to validate the sewage-house-prices replication package in [replication directory or 'here'] and produce a 10-check AEA-style replication audit report saved to the documented log location.
Required inputs
[replication directory or 'here']
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SKILL.md
name: audit-replicationdescription: Validate the replication package for the sewage-house-prices project. Runs 10 checks covering script execution, file integrity, output freshness, dependency verification, data provenance, and README completeness (AEA format). This skill should be used when asked to "audit replication", "check the package", or "verify reproducibility".argument-hint: "[replication directory OR 'here' for current project]"allowed-tools: ["Read", "Grep", "Glob", "Write", "Bash", "Agent"]
Audit Replication Package
Run end-to-end validation of the replication package for the sewage-house-prices project.
Input:$ARGUMENTS — directory containing the replication package (Replication/ by default), or here for the current project root.
Project-Specific Context
Expected Structure
Pipeline scripts: scripts/R/01_data_ingestion/ through scripts/R/06_analysis_datasets/
Analysis scripts: scripts/R/09_analysis/
Utilities: scripts/R/utils/
Python: scripts/python/
Data: data/raw/, data/processed/, data/final/
Output: output/tables/, output/figures/
Manuscript: docs/overleaf/
R packages: renv.lock
Workflow
Step 1: Locate Package
If $ARGUMENTS is a directory path: use it
If $ARGUMENTS is here or empty: use Replication/ if it exists, otherwise project root
Verify the directory exists and contains scripts
Step 2: Run 10 Verification Checks
Standard Checks (1-4)
Script syntax — All .R files parse without syntax errors
File references — All read_parquet(), readRDS(), read.csv() targets exist or are documented as inputs
File integrity — No orphaned scripts (referenced but missing), no orphaned outputs (generated but unreferenced)
Output freshness — Output files are newer than their source scripts (no stale outputs)
Submission Checks (5-10)
Package inventory — Scripts are logically ordered, master script exists
Dependency verification — renv.lock present, R version documented, Python deps documented
Data provenance — Every dataset has documented source, access date, and restrictions
Path hygiene — No hardcoded absolute paths, all paths use here::here()
Output cross-reference — Every table/figure in the manuscript traces to a specific script
README completeness — AEA format: data availability, computational requirements, program descriptions, instructions
Step 3: Handle Failures
If failures found:
Display specific error with file and line number
Suggest concrete fix
Ask user if they want to fix and re-audit (max 3 iterations)