Code Comprehension Report

Code Comprehension Report — Mental Model Generation

Addresses AI-generated code opacity. After each SDD dev-session, auto-generate a mental model document explaining implementation decisions, failure points, debugging heuristics, and implicit dependencies.

When to Use

• After implementing a feature (post-SDD completion)
• After fixing a complex bug
• When onboarding new team members to undocumented code
• When code lacks sufficient inline documentation
• User asks to /comprehension-report {task-id}

7-Phase Pipeline

Phase 1: Collect Implementation Data (5 min)

• Input: spec path, git commit hash, or task ID
• Collect: SDD spec, implemented code files, test results, agent notes
• Verify: code compiles, tests pass, spec is complete
• Store: in output/dev-sessions/{task-id}/phase-1-data.md

Phase 2: Architecture Decisions (10 min)

• List each decision: made during implementation
• For each decision:
  • Why it was chosen (trade-offs considered)
  • Alternatives discarded (with reason)
  • Key assumptions underlying the decision
  • Risks or caveats if violated

Output: table format with Decision | Rationale | Alternatives | Risks

Phase 3: Flow Diagram (5 min)

• Generate Mermaid diagram of the change:
  • Data flow (inputs → processing → outputs)
  • Call chain (entry points → internal calls → external deps)
  • State transitions if applicable
  • External integrations highlighted

Output: .mermaid file embedded in report + PNG export

Phase 4: Failure Heuristics (15 min)

For each module touched: "If this fails, it's probably X. Look at Y. Key metric: Z"

Template: see references/schemas.md

Phase 5: Implicit Dependencies (8 min)

List dependencies introduced that aren't obvious from imports:

• Runtime deps: required services, databases, caches (not just NuGet)
• Config deps: environment variables, feature flags, settings
• Data format assumptions: field ordering, encoding, version compatibility
• External service deps: third-party APIs, webhooks, message queues
• Timing deps: race conditions, retry policies, timeouts

Format: table with Dependency Type | What's Required | Impact if Missing

Phase 6: 3AM Debugging Guide (12 min)

Concrete steps an on-call engineer would follow to diagnose issues at 3 AM without context:

Step-by-step procedures:

1. Verify prerequisites (service running, DB accessible, env vars set)
2. Check logs at key points (entry, error handling, exit)
3. Inspect state (cache state, queue depth, last transaction)
4. Common fixes (restart service, clear cache, check disk space)
5. Escalation path (who to call, what to provide)

For each common failure scenario:

• Symptom (what the user reports)
• Immediate check (5 min diagnosis)
• Root cause areas (3-5 places to look)
• Fix (if it's a quick win) or escalation

Phase 7: Generate Report (5 min)

• Compile all phases into single markdown document
• Save to: output/comprehension/YYYYMMDD-{task-id}-mental-model.md
• Format:
  • Summary (1 page TL;DR)
  • Architecture decisions (1 page)
  • Flow diagram (visual)
  • Failure heuristics (2 pages, by module)
  • Implicit dependencies (1 page)
  • 3AM guide (2 pages)
  • Appendix: agent notes, spec excerpt
• Quality check: coherence validator confirms completeness

Schemas

Input/output schemas and templates: references/schemas.md

Quality Gates

• Phase 1: All input files exist and are readable
• Phase 2: ≥3 decisions documented, each with alternatives
• Phase 3: Mermaid diagram renders without error
• Phase 4: ≥2 failure heuristics per module touched
• Phase 5: ≥5 implicit dependencies documented
• Phase 6: ≥3 steps per common scenario, escalation clear
• Phase 7: Report ≤ 15 pages, coherence ≥ 85%

Limitations

• Does NOT re-implement the feature (read-only operation)
• Does NOT modify code or specs
• Assumes code compiles and tests pass
• Spanish user-facing, technical content may be English (code comments, schema names)

Integration

Triggered by:

• /comprehension-report {task-id} — generate on demand
• /dev-session auto-completion (optional post-session)
• /spec-completion → "Generate mental model? [y/n]"

Used by:

• Team onboarding: new developers understand decisions + caveats
• Postmortem analysis: why a bug occurred, prevented mechanisms
• Code review: reviewers understand intent before reading code

Related Skills

• .opencode/skills/spec-driven-development/SKILL.md — generates specs that feed this skill
• .opencode/skills/code-review/SKILL.md — uses comprehension as context for better reviews