End-to-end data analysis dispatching Coder and Data-engineer for implementation, coder-critic for review. Supports R, Stata, Python, Julia. Replaces /data-analysis.
SKILL.md
Analyze
Run end-to-end data analysis by dispatching the Coder (analysis), Data-engineer (cleaning + figures), and coder-critic (code review).
Input:$ARGUMENTS — dataset path or description of analysis goal.
Workflow
Step 1: Context Gathering
Read .claude/references/domain-profile.md for field conventions
Read strategy memo in quality_reports/ if it exists
Check CLAUDE.md for language preference (R/Stata/Python/Julia)
Scan existing scripts in scripts/ for project patterns
Step 2: Data Preparation (if needed)
If raw data provided, dispatch Data-engineer first:
Clean and wrangle raw data
Handle missing values, construct variables per strategy memo
Generate summary statistics table
Create publication-quality descriptive figures
Save cleaned data, codebook, and figures
Step 3: Main Analysis
Dispatch Coder agent:
Stage 0: Data loading (from cleaned data or raw)
Stage 1: Main specification (from strategy memo or user description)
Stage 2: Robustness checks
Stage 3: Publication-ready output (tables to paper/tables/, figures to paper/figures/)
Produce results_summary.md with all estimates, SEs, and key statistics (MANDATORY)
Save scripts to scripts/R/ (or appropriate language directory)
The Coder follows these principles:
Script structure: Use the Script Structure Template below
Packages:fixest for panel data, for tables, for figures
modelsummary
ggplot2
Standard errors: Cluster at appropriate level (match treatment assignment)
Output:.tex tables for LaTeX, .pdf/.png figures, .rds for intermediate objects
No hardcoded paths. All paths relative to repository root.
saveRDS everything. Every computed object (estimates, model fits, data frames, summary statistics) gets serialized to .rds for downstream use by the writer and other agents.
Step 4: Code Review
Dispatch coder-critic agent — run the full 12-category checklist:
Strategic (categories 1-3):
Code-strategy alignment — Does the code implement the strategy memo faithfully? Correct dependent variable, treatment, controls, fixed effects, sample restrictions?
Sanity checks — Are summary statistics printed before regressions? Do coefficient signs match economic intuition? Are sample sizes reasonable?
Robustness sufficiency — Are required robustness checks present? Alternative specifications, placebo tests, sensitivity analysis per strategy memo?
Code Quality (categories 4-12):
4. Structure — Does the script follow the standard template? Clear section headers, logical flow from setup to export?
5. Console hygiene — No spurious print() statements polluting output. Intentional output only.
6. Reproducibility — set.seed() at top if any stochastic elements. No absolute paths. All packages loaded at top. Directory creation with showWarnings = FALSE.
7. Functions — Repeated logic extracted into functions. No copy-paste code blocks with minor variations.
8. Figure quality — Publication-ready: proper axis labels, titles, legends, font sizes. Consistent theme across all figures.
9. RDS pattern — Every computed object (models, data frames, summary stats) saved via saveRDS() for downstream use. Not just final outputs — intermediate objects too.
10. Comments — Section headers present. Non-obvious code commented. No commented-out dead code left behind.
11. Error handling — Graceful handling of missing files, empty data subsets, convergence failures. Informative error messages.
12. Polish — Consistent naming conventions. No magic numbers. Clean whitespace. Professional quality ready for replication package.
If strategy memo exists, cross-reference code against stated design.
Save report to quality_reports/[script]_code_review.md.
Step 5: Fix Issues
If coder-critic finds Critical or Major issues:
Re-dispatch Coder with specific fixes (max 3 rounds)
Re-run coder-critic to verify fixes
Step 6: Present Results
Results summary — key estimates with SEs and interpretation (from results_summary.md)
Scripts created — paths and descriptions
Output files — tables in paper/tables/, figures in paper/figures/
Code review score — from coder-critic
TODO items — missing data, additional specifications needed
Every analysis run MUST produce results_summary.md containing:
All point estimates with standard errors and significance levels
Sample sizes for each specification
Key summary statistics (means, medians, standard deviations of main variables)
Robustness check results (brief table or comparison)
Any flags or anomalies discovered during analysis
This file is the primary handoff artifact to the writer agent. Without it, the writer cannot draft the results section.
Dual-Language Mode (--dual r,python)
When --dual [lang1,lang2] is provided (e.g., --dual r,python, --dual r,stata):
Data-engineer runs once — language-agnostic cleaning, saves to data/cleaned/
Two Coder agents dispatched in parallel — same strategy memo, different languages
coder-critic reviews each implementation independently (max 3 rounds each)
Comparison step — verify numerical alignment per .claude/references/domain-profile.md tolerances:
Point estimates must match within declared tolerance
Standard errors must match within declared tolerance
Flag any divergences with exact values from both languages
Save comparison report to quality_reports/cross_language_comparison.md
Replication Tolerance Approach
Inspired by Scott Cunningham's replication methodology: if two independent implementations agree, neither has a bug. This is the core rationale for dual-language mode.
Tolerance thresholds:
Floating-point differences are normal. Minor numerical differences (e.g., 1e-10) between R and Python/Stata arise from different linear algebra backends, optimizer defaults, and floating-point arithmetic. These are expected, not bugs.
Point estimates: Must agree within 1e-6 (relative) or as declared in domain-profile.md
Standard errors: Must agree within 1e-4 (relative) — SE computation varies more across implementations due to degrees-of-freedom corrections and clustering algorithms
P-values: Must agree on significance at conventional levels (0.01, 0.05, 0.10). If one language says p=0.049 and the other says p=0.051, flag for manual review but do not treat as a bug.
Sample sizes: Must match exactly. Any discrepancy indicates a data handling difference that must be resolved.
When results diverge beyond tolerance:
Both Coder agents are re-dispatched to investigate
Check: different default options (e.g., na.rm handling, convergence criteria)
Check: different variable coding or factor ordering
The comparison report includes a side-by-side table of all estimates
If divergence persists after investigation, escalate to user with exact values from both languages
Principles
Reproduce, don't guess. If the user specifies a regression, run exactly that.
Show your work. Print summary statistics before jumping to regressions.
Strategy alignment. If strategy memo exists, code MUST implement it faithfully.
Worker-critic pairing. Coder creates, coder-critic critiques. Never skip review.
saveRDS everything. Every computed object gets saved via saveRDS() for downstream use — model fits, cleaned data frames, summary statistics, not just final tables.
Publication-ready output. Tables and figures directly includable in the paper.
Cross-language convergence. When --dual is used, divergence is a bug until proven otherwise.
