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disposable-autopsy

Install
1
Install the plugin
$
npx claudepluginhub caphtech/claude-marketplace --plugin disposable-plugin

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Then install: npx claudepluginhub u/[userId]/[slug]

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.

Tool Access

This skill uses the workspace's default tool permissions.

Skill Content

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:

MetricMaps to Axis
lint.error countcorrectness, readability
tests.failedcorrectness, error-handling
tests.passed / tests.totaltestability
coverage.line.pcttestability, maintainability
coverage.branch.pcterror-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 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 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 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)
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Last CommitMar 5, 2026

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