/compound

Pipeline mode: This command operates fully autonomously. All decisions are made automatically.

Coordinate multiple subagents working in parallel to document a recently solved research problem. Creates structured documentation in docs/solutions/ with YAML frontmatter for searchability and future reference.

Purpose

Captures problem solutions while context is fresh. Uses parallel subagents for maximum efficiency — Phase 1 gathers information, Phase 2 assembles the final document.

Why "compound"? Each documented solution compounds your methodological knowledge. The first time you solve a convergence problem takes hours of research. Document it, and the next occurrence takes minutes. Knowledge compounds.

Usage

bash
/workflows:compound                          # Document the most recent fix
/workflows:compound convergence failure in BLP inner loop  # Provide context
/workflows:compound fixed cluster-robust SEs  # Brief description

Execution Strategy: Two-Phase Orchestration

<critical_requirement> Only ONE file gets written — the final documentation.

Phase 1 subagents return TEXT DATA to the orchestrator. They must NOT use Write, Edit, or create any files. Only the orchestrator (Phase 2) writes the final documentation file. </critical_requirement>

Phase 1: Parallel Research

<parallel_tasks>

Launch these subagents IN PARALLEL. Each returns text data to the orchestrator.

1. Context Analyzer

• Extracts conversation history for the problem-solving session
• Identifies problem type, estimation method, symptoms, error messages
• Auto-categorizes the problem (see Category Classification below)
• Returns: YAML frontmatter skeleton with problem metadata

2. Solution Extractor

• Analyzes all investigation steps taken during the session
• Identifies root cause (e.g., "ill-conditioned Hessian due to poor starting values")
• Extracts working solution with code examples
• Documents what didn't work and why (important for future reference)
• Returns: Solution content block with code snippets

3. Related Docs Finder

• Searches docs/solutions/ for related documentation
• Identifies cross-references and links to similar problems
• Checks if this problem is a variant of a previously documented issue
• Returns: Links, relationships, and duplicate-avoidance notes

4. Prevention Strategist

• Develops prevention strategies specific to the problem type
• Creates diagnostic checklist ("check these things first next time")
• Suggests robustness checks or tests that would catch this early
• Returns: Prevention/diagnostic content

5. Category Classifier

• Auto-detects the appropriate docs/solutions/ category from problem description and session content
• Validates category against the schema below
• Generates filename slug from problem description
• Returns: Final path and filename

</parallel_tasks>

Category Classification

Problems are auto-classified into one or more categories using keyword matching on the problem description and session content:

| Category | Directory | Keywords / Signals | |----------|-----------|-------------------| | Estimation Issues | estimation-issues/ | convergence, bias, efficiency, standard errors, MLE, GMM, likelihood, optimizer, starting values, boundary, gradient, Hessian | | Data Issues | data-issues/ | missing data, measurement error, sample selection, merge, duplicates, outliers, panel structure, encoding, cleaning | | Numerical Issues | numerical-issues/ | floating-point, overflow, underflow, condition number, tolerance, ill-conditioning, precision, NaN, Inf, singular matrix | | Methodology Issues | methodology-issues/ | identification, model specification, assumption violations, endogeneity, exclusion restriction, functional form, overidentification | | Derivation Issues | derivation-issues/ | proof, theorem, lemma, asymptotic, regularity conditions, existence, uniqueness, fixed point, convergence rate | | Replication Issues | replication-issues/ | reproducibility, package versions, seeds, environment, Docker, conda, renv, pipeline, Makefile, DVC |

Multi-category problems: A problem can belong to multiple categories (e.g., "BLP convergence failure" is both estimation-issues/ and numerical-issues/). Use the primary category for the file location and cross-reference the secondary category in the frontmatter tags field.

Ambiguous problems: If keyword matching is inconclusive, default to methodology-issues/ (the broadest category).

Phase 2: Assembly & Write

<sequential_tasks>

WAIT for all Phase 1 subagents to complete before proceeding.

The orchestrating agent performs these steps:

1. Collect all text results from Phase 1 subagents
2. Assemble complete markdown file using the template below
3. Validate YAML frontmatter fields are complete
4. Create directory if needed: mkdir -p docs/solutions/[category]/
5. Write the SINGLE final file: docs/solutions/[category]/[filename].md

Documentation Template

markdown
---
title: "[Problem title — concise, searchable]"
date: YYYY-MM-DD
category: [primary category]
tags: [estimation, convergence, BLP, ...]
estimation_method: [if applicable: MLE, GMM, IV, DiD, ...]
language: [Python, R, Julia, Stata]
severity: [critical, moderate, minor]
time_to_resolve: [approximate time spent]
---

# [Problem Title]

## Problem

**Symptom:** [What was observed — error messages, wrong results, failure to converge]

**Context:** [What estimation/analysis was being performed, what data, what method]

**Reproduction:** [Minimal steps to reproduce the problem]

## Investigation

### What Didn't Work
1. [Attempted fix and why it failed]
2. [Another attempt and outcome]

### Root Cause
[Technical explanation of why the problem occurred]

## Solution

[Step-by-step fix with code examples]

or R, Julia, Stata as appropriate

Working code with comments explaining the fix

## Prevention

**Diagnostic Checklist** (check these first next time):
- [ ] [First thing to verify]
- [ ] [Second thing to verify]
- [ ] [Third thing to verify]

**Robustness Checks:**
- [Checks that would catch this early]

## Related

- [Links to related docs/solutions/ entries]
- [Links to methodology papers if relevant]
- [Links to package documentation]

</sequential_tasks>

Phase 3: Specialized Agent Review (Optional)

WAIT for Phase 2 to complete before proceeding.

Based on the problem category, optionally invoke a specialized agent to review the documentation for accuracy and completeness:

<parallel_tasks>

| Problem Category | Agent | Review Focus | |-----------------|-------|-------------| | estimation-issues/ | econometric-reviewer | Solution correctness, estimation theory | | numerical-issues/ | numerical-auditor | Numerical accuracy, stability claims | | methodology-issues/ | methods-explorer | Methodological completeness, alternatives | | derivation-issues/ | mathematical-prover | Proof correctness, regularity conditions | | data-issues/ | data-detective | Data handling best practices | | replication-issues/ | reproducibility-auditor | Reproducibility completeness |

Launch the matching agent(s) to verify the documented solution is correct and complete. If the agent finds issues, update the documentation file.

</parallel_tasks>

Phase 4: Knowledge Capture

Use references/solution-schema.md to ensure the solution is properly indexed and cross-referenced:

• Verify the documentation follows the YAML frontmatter schema
• Cross-reference with existing docs/solutions/ entries
• Detect patterns (3+ similar issues) and promote critical patterns
• Update any index or search metadata

What It Captures

• Problem symptom: Exact error messages, observable behavior, numerical output
• Investigation steps tried: What didn't work and why (often the most valuable part)
• Root cause analysis: Technical explanation grounded in estimation/statistical theory
• Working solution: Step-by-step fix with complete, runnable code examples
• Prevention strategies: Diagnostic checklists and robustness checks
• Cross-references: Links to related solutions, methodology papers, package docs

What It Creates

Organized documentation:

• File: docs/solutions/[category]/[filename].md
• Categories auto-detected from problem description

Category directories:

• estimation-issues/ — convergence failures, biased estimates, wrong standard errors
• data-issues/ — missing data, merge errors, sample selection problems
• numerical-issues/ — floating-point, ill-conditioning, overflow/underflow
• methodology-issues/ — identification failures, specification errors, assumption violations
• derivation-issues/ — proof errors, incorrect asymptotics, missing regularity conditions
• replication-issues/ — reproducibility failures, environment issues, missing documentation

Common Mistakes to Avoid

| Wrong | Correct | |-------|---------| | Subagents write files like context-analysis.md | Subagents return text data; orchestrator writes one final file | | Research and assembly run in parallel | Research completes, THEN assembly runs | | Multiple files created during workflow | Single file: docs/solutions/[category]/[filename].md | | Generic description ("fixed the bug") | Specific description ("replaced numerical Hessian with analytic Hessian to fix ill-conditioning in BLP inner loop") | | Solution without code | Always include runnable code showing the fix | | Skipping "what didn't work" | Failed approaches are the most valuable for future reference |

Success Output

Documentation complete

Subagent Results:
  Context Analyzer: Identified numerical_issue in BLP inner loop
  Solution Extractor: Analytic Hessian fix with code example
  Related Docs Finder: 1 related entry (estimation-issues/blp-starting-values.md)
  Prevention Strategist: 4-item diagnostic checklist
  Category Classifier: numerical-issues/

Specialized Review:
  numerical-auditor: Verified solution correctness, confirmed condition number improvement

Knowledge Capture:
  Solution schema: Indexed and cross-referenced

File created:
  docs/solutions/numerical-issues/blp-inner-loop-hessian-conditioning.md

This solution will be searchable for future reference when similar
numerical stability issues occur in BLP estimation.

Next steps:
1. Continue workflow — run /workflows:review for remaining changes
2. Link related documentation if other solutions should reference this
3. Run /workflows:work if additional implementation is needed

The Compounding Philosophy

This creates a compounding knowledge system for research methodology:

1. First time you solve "ill-conditioned Hessian in BLP" → Research and debug (2 hours)
2. Document the solution → docs/solutions/numerical-issues/blp-hessian.md (5 min)
3. Next time similar issue occurs → Quick lookup via docs/solutions/ search (2 min)
4. Knowledge compounds → Research team gets faster at diagnosing and fixing estimation problems

The feedback loop:

Specify Model → Estimate → Diagnose Issue → Research → Fix → Document → Validate
     ↑                                                                       ↓
     └───────────────────────────────────────────────────────────────────────┘

Each unit of methodological problem-solving should make subsequent units easier — not harder.

Auto-Invoke

This command is most valuable immediately after solving a non-trivial problem. Trigger phrases:

• "that converges now"
• "fixed the estimation"
• "identification argument is complete"
• "pipeline runs end-to-end"
• "replication package works"

Or invoke directly: /workflows:compound [brief context]

Phase 5: Handoff

Pipeline mode (when invoked from /lfg or /slfg):

• This is the final step in the chain. Report completion and stop.
• Output: "Workflow complete. Solution documented at path]."

Standalone mode (when invoked directly by the user):

• After the documentation is written, present options:

1. Start next cycle — Run /workflows:brainstorm to begin a new research cycle
2. Review the documentation — Read back the docs/solutions/ file just created
3. End session — Stop here; the solution is documented

This closes the loop: compound → brainstorm → plan → work → review → compound.

Applicable Specialized Agents

Based on problem category, these agents enhance and verify documentation:

Estimation & Methodology

• econometric-reviewer: Reviews estimation-issue solutions for theoretical correctness
• methods-explorer: Enriches with alternative approaches and literature references
• identification-critic: Verifies identification-related solutions are complete

Numerical & Data

• numerical-auditor: Validates numerical stability claims and solutions
• data-detective: Reviews data-issue solutions for completeness

Rigor & Reproducibility

• mathematical-prover: Checks derivation-issue solutions for proof correctness
• reproducibility-auditor: Validates replication-issue solutions

Configuration

Customize which review agents run by editing compound-science.local.md.