disposable-autopsy

name	disposable-autopsy
context	fork
argument-hint	[cycle_N]
description	Analyze a disposable prototype across 10 quality axes using static analysis, test results, and Codex MCP triangulation. Produces structured autopsy report with scored findings and recommendations. Part of H-DGM cycle. Use after disposable-spike completes.

Disposable Autopsy — Phase 2: Analyze

Perform 10-axis analysis of a disposable prototype, combining quantitative metrics with qualitative AI review.

Prerequisites

• Completed spike: .disposable/cycles/cycle_{N}/spike-complete.json must exist
• Spike branch disposable/cycle_{N} must exist
• Codex MCP available for triangulated review (optional but recommended)

Procedure

Step 1: Load Spike Context

1. Determine cycle: use $ARGUMENTS if provided, otherwise read latest from .disposable/history.json
2. Load metrics from .disposable/cycles/cycle_{N}/spike-complete.json
3. Checkout spike branch: git checkout disposable/cycle_{N}
4. Read generated source files for analysis

Step 2: Static Analysis (Quantitative)

Extract quantitative signals from metrics:

Metric	Maps to Axis
lint.error count	correctness, readability
tests.failed	correctness, error-handling
tests.passed / tests.total	testability
coverage.line.pct	testability, maintainability
coverage.branch.pct	error-handling

Step 3: Qualitative Analysis (10 Axes)

Analyze the prototype code against each axis. For each axis:

1. correctness — Does the code do what was specified? Check requirements coverage, logic errors
2. architecture — Module boundaries, dependency direction, separation of concerns
3. security — Input validation, injection risks, auth boundaries, secret handling
4. performance — Algorithmic complexity, unnecessary allocations, N+1 patterns
5. testability — Test isolation, mock-ability, deterministic behavior
6. readability — Naming, function length, cognitive complexity
7. maintainability — DRY, coupling metrics, change amplification risk
8. error-handling — Error propagation, recovery paths, fail-fast behavior
9. dependency-hygiene — Minimal dependencies, version constraints, license compatibility
10. documentation — API contracts, non-obvious behavior, setup instructions

For each axis, assign:

• status: scored | na | insufficient-evidence
• score: 1-5 (when scored)
  • 1 = Critical issues, fundamentally broken
  • 2 = Major issues, significant rework needed
  • 3 = Acceptable, typical for rapid prototype
  • 4 = Good, minor improvements only
  • 5 = Excellent, production-ready quality
• findings[]: Specific issues with severity and evidence reference
• recommendations[]: Actionable improvements with priority

Step 4: Triangulated Review (Optional)

If Codex MCP is available, request independent review:

mcp__codex__codex(
  prompt: "Review the following disposable prototype for {axis}.
  Focus on: {axis-specific criteria}.
  Report findings as JSON array with id, severity, description, evidenceRef fields.
  Files: {file list}",
  model: "gpt-5.4",
  config: { "model_reasoning_effort": "xhigh" },
  cwd: "{project_root}"
)

Merge Codex findings with Claude findings:

• Findings reported by both → increase confidence (severity stays or escalates)
• Findings reported by only one → keep but flag as single-source
• Contradictions → note in findings, use Claude's judgment for final score

Step 5: Determine Verdict

Apply quality gates from {plugin_root}/skills/disposable-cycle/references/quality-gates.md:

1. Calculate averageScore from all scored axes
2. Check each gate condition against metrics and scores
3. Assign verdict: PASS | CALIBRATE | FAIL

Step 6: Generate Report

Construct autopsy report following {plugin_root}/skills/disposable-cycle/references/autopsy-schema.json:

{
  "schemaVersion": "1.0.0",
  "rubricVersion": "1.0.0",
  "cycleId": "cycle_{N}",
  "timestamp": "{ISO 8601}",
  "metricsRef": "spike-complete.json",
  "axes": { ... },
  "summary": {
    "verdict": "PASS|CALIBRATE|FAIL",
    "strengths": [...],
    "criticalIssues": [...],
    "averageScore": N.N
  }
}

Step 7: Save, Validate & Mask

1. Save report to .disposable/cycles/cycle_{N}/autopsy-report.json
2. Validate report against schema:

   node {plugin_root}/scripts/dist/validate-report.mjs \
     .disposable/cycles/cycle_{N}/autopsy-report.json \
     --schema {plugin_root}/skills/disposable-cycle/references/autopsy-schema.json
   

3. If validation fails: fix report structure and re-validate (max 2 retries)
4. Mask sensitive data:

   node {plugin_root}/scripts/dist/mask-sensitive.mjs \
     .disposable/cycles/cycle_{N}/autopsy-report.json --in-place
   

5. Return to original branch: git checkout -

Step 8: Report to User

Present summary:

• Verdict with confidence level
• Top 3 strengths
• Critical issues requiring attention
• Axis scores table
• Recommendation for next step: /disposable-distill or /disposable-cycle to iterate

Output

• .disposable/cycles/cycle_{N}/autopsy-report.json — validated autopsy report
• Ready for /disposable-distill

Error Handling

• If metrics file is missing: check data completeness. If tests are unavailable, set verdict to FAIL per {plugin_root}/skills/disposable-cycle/references/quality-gates.md. For lint/coverage only, mark affected axes as insufficient-evidence and continue
• If Codex MCP is unavailable: proceed with Claude-only analysis, note in report
• If schema validation fails: fix report structure, re-validate (max 2 retries)