/compound

/compound

Coordinate multiple subagents working in parallel to document a recently solved problem.

Purpose

Captures problem solutions while context is fresh, creating structured documentation in docs/solutions/ with YAML frontmatter for searchability and future reference. Uses parallel subagents for maximum efficiency.

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

Usage

/workflows:compound                    # Document the most recent fix
/workflows:compound [brief context]    # Provide additional context hint

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
• Identifies problem type, component, symptoms
• Validates against schema
• Returns: YAML frontmatter skeleton

2. Solution Extractor

• Analyzes all investigation steps
• Identifies root cause
• Extracts working solution with code examples
• Returns: Solution content block

3. Related Docs Finder

• Searches docs/solutions/ for related documentation
• Identifies cross-references and links
• Finds related GitHub issues
• Returns: Links and relationships

4. Prevention Strategist

• Develops prevention strategies
• Creates best practices guidance
• Generates test cases if applicable
• Returns: Prevention/testing content

5. Category Classifier

• Determines optimal docs/solutions/ category
• Validates category against schema
• Suggests filename based on slug
• Returns: Final path and filename

</parallel_tasks>

Phase 2: Assembly & Write

<sequential_tasks>

WAIT for all Phase 1 subagents to complete before proceeding.

The orchestrating agent (main conversation) performs these steps:

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

</sequential_tasks>

Phase 3: Optional Enhancement

WAIT for Phase 2 to complete before proceeding.

<parallel_tasks>

Optionally invoke available research or design agents to review the documentation:

• best-practices-researcher → Enrich with industry best practices
• framework-docs-researcher → Link to framework documentation references

</parallel_tasks>

What It Captures

• Problem symptom: Exact error messages, observable behavior
• Investigation steps tried: What didn't work and why
• Root cause analysis: Technical explanation
• Working solution: Step-by-step fix with code examples
• Prevention strategies: How to avoid in future
• Cross-references: Links to related issues and docs

Preconditions

<preconditions enforcement="advisory"> <check condition="problem_solved"> Problem has been solved (not in-progress) </check> <check condition="solution_verified"> Solution has been verified working </check> <check condition="non_trivial"> Non-trivial problem (not simple typo or obvious error) </check> </preconditions>

What It Creates

Organized documentation:

• File: docs/solutions/[category]/[filename].md

Categories auto-detected from problem:

• build-errors/
• test-failures/
• runtime-errors/
• performance-issues/
• database-issues/
• security-issues/
• ui-bugs/
• integration-issues/
• logic-errors/

Common Mistakes to Avoid

❌ Wrong	✅ Correct
Subagents write files like context-analysis.md, solution-draft.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

Success Output

✓ Documentation complete

Subagent Results:
  ✓ Context Analyzer: Identified performance_issue in brief_system
  ✓ Solution Extractor: 3 code fixes
  ✓ Related Docs Finder: 2 related issues
  ✓ Prevention Strategist: Prevention strategies, test suggestions
  ✓ Category Classifier: `performance-issues`

Specialized Agent Reviews (Auto-Triggered):
  ✓ best-practices-researcher: Enriched with industry best practices
  ✓ framework-docs-researcher: Linked to Rails documentation references

File created:
- docs/solutions/performance-issues/n-plus-one-brief-generation.md

This documentation will be searchable for future reference when similar
issues occur in the Email Processing or Brief System modules.

What's next?
1. Continue workflow (recommended)
2. Link related documentation
3. Update other references
4. View documentation
5. Other

The Compounding Philosophy

This creates a compounding knowledge system:

1. First time you solve "N+1 query in brief generation" → Research (30 min)
2. Document the solution → docs/solutions/performance-issues/n-plus-one-briefs.md (5 min)
3. Next time similar issue occurs → Quick lookup (2 min)
4. Knowledge compounds → Team gets smarter

The feedback loop:

Build → Test → Find Issue → Research → Improve → Document → Validate → Deploy
    ↑                                                                      ↓
    └──────────────────────────────────────────────────────────────────────┘

Each unit of engineering work should make subsequent units of work easier—not harder.

Auto-Invoke

<auto_invoke> <trigger_phrases> - "that worked" - "it's fixed" - "working now" - "problem solved" </trigger_phrases>

<manual_override> Use /workflows:compound [context] to document immediately without waiting for auto-detection. </manual_override> </auto_invoke>

Routes To

compound-docs skill

Applicable Specialized Agents

Based on problem type, these agents can enhance documentation:

Research & Enhancement

• best-practices-researcher: Enriches solution with industry best practices
• framework-docs-researcher: Links to Rails/gem documentation references

When to Invoke

• Auto-triggered (optional): Agents can run post-documentation for enhancement
• Manual trigger: User can invoke agents after /workflows:compound completes for deeper review

Related Commands

• /research [topic] - Deep investigation (searches docs/solutions/ for patterns)
• /workflows:plan - Planning workflow (references documented solutions)