analyze

name	analyze
description	End-to-end data analysis dispatching Coder and Data-engineer for implementation, coder-critic for review. Supports R, Python, Julia. Replaces /data-analysis.
argument-hint	[dataset path or goal] Options: --dual [lang1,lang2]
allowed-tools	Read,Grep,Glob,Write,Edit,Bash,Task

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: Pre-Code Report (mandatory)

Before writing any code, the Coder must output a structured report proving it read the strategy inputs:

## Pre-Code Report
**Strategy memo:** [path or "not found"]
**Domain profile:** [loaded / not found]
**Language:** [R / Python / Julia — from CLAUDE.md]
**Paper type:** [reduced-form / structural / theory+empirics / descriptive]

**Identification strategy:** [one sentence from memo]
**Key variables:**
- Outcome: [paper name] → [code name]
- Treatment: [paper name] → [code name]
- Controls: [list]
- Fixed effects: [list]
- Clustering: [level]
**Data source:** [path or description]
**Estimator:** [from strategy memo]
**Robustness checks required:** [list from memo]
**Naming map confirms:** [yes / no — do planned code names match paper notation?]

Proceeding to implementation.

If the strategy memo is missing, the Coder proceeds with the user's description — but flags that no memo was found and strategic alignment checks (coder-critic categories 1-3) cannot be verified.

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, modelsummary for tables, ggplot2 for figures
• 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):

1. Code-strategy alignment — Does the code implement the strategy memo faithfully? Correct dependent variable, treatment, controls, fixed effects, sample restrictions?
2. Sanity checks — Are summary statistics printed before regressions? Do coefficient signs match economic intuition? Are sample sizes reasonable?
3. 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:

1. Re-dispatch Coder with specific fixes (max 3 rounds)
2. Re-run coder-critic to verify fixes

Step 6: Present Results

1. Results summary — key estimates with SEs and interpretation (from results_summary.md)
2. Scripts created — paths and descriptions
3. Output files — tables in paper/tables/, figures in paper/figures/
4. Code review score — from coder-critic
5. TODO items — missing data, additional specifications needed

---

Script Structure Template

# ============================================================
# [Descriptive Title]
# Author: [from project context]
# Purpose: [What this script does]
# Inputs: [Data files]
# Outputs: [Figures, tables, RDS files]
# ============================================================

# 0. Setup ----
library(tidyverse)
library(fixest)
library(modelsummary)

set.seed(42)

dir.create("paper/tables", recursive = TRUE, showWarnings = FALSE)
dir.create("paper/figures", recursive = TRUE, showWarnings = FALSE)

# 1. Data Loading ----

# 2. Exploratory Analysis ----

# 3. Main Analysis ----

# 4. Tables and Figures ----

# 5. Export ----
# saveRDS(model_fit, "scripts/R/output/model_fit.rds")
# saveRDS(main_results, "scripts/R/output/main_results.rds")

---

Results Summary (Mandatory Artifact)

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,julia):

1. Data-engineer runs once — language-agnostic cleaning, saves to data/cleaned/
2. Two Coder agents dispatched in parallel — same strategy memo, different languages
3. coder-critic reviews each implementation independently (max 3 rounds each)
4. 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
5. 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 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:

1. Both Coder agents are re-dispatched to investigate
2. Check: different default options (e.g., na.rm handling, convergence criteria)
3. Check: different variable coding or factor ordering
4. The comparison report includes a side-by-side table of all estimates
5. 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.