Imported from jasonmichaelbell78-creator/sonash-v0 (.claude/skills/audit-enhancements/SKILL.md). Install upstream with npx skills add jasonmichaelbell78-creator/sonash-v0 --skill audit-enhancements. Copyright stays with the author.

Document Version: 1.2 Last Updated: 2026-02-23 Status: ACTIVE

Enhancement Audit

When to Use

• Tasks related to audit-enhancements
• User explicitly invokes /audit-enhancements

When NOT to Use

• When the task doesn't match this skill's scope -- check related skills
• When a more specialized skill exists for the specific task

Overview

Performs a comprehensive, multi-pass enhancement audit of the entire project — not just code, but also product/UX, content, workflows, infrastructure, external services, and the audit system itself. Produces findings in the Technical Debt Management System (TDMS) with category: "enhancements" and type: "enhancement", including mandatory honesty guardrails.

Key differentiator from other audits: This audit looks for things that could be better, not things that are wrong. Every finding includes a mandatory counter-argument explaining why NOT to make the change.

Architecture: 4-Phase Adaptive Model

Phase 1: Broad Scan (8 parallel agents, one per domain)
  → Each agent surveys its domain, produces preliminary findings
  → Output: stage-1-*.jsonl (preliminary findings per domain)

Phase 2: Deep Dive (OPT-IN — user decides after seeing Phase 1 results)
  → Agents spawned for top-opportunity areas identified in Phase 1
  → Includes competitor/peer benchmarking via web search
  → Output: stage-2-*.jsonl (deep findings with full schema)

Phase 3: Cross-Cutting Synthesis (1-2 sequential agents)
  → Cross-domain pattern detection, TDMS cross-referencing
  → Dedup, consolidation, Strengths section compilation
  → Output: all-findings-deduped.jsonl + ENHANCEMENT_AUDIT_REPORT.md

Phase 4: Interactive Review (conversational, spans sessions if needed)
  → One-by-one walkthrough of findings
  → User decides: ACCEPTED / DECLINED / DEFERRED
  → Output: Updated MASTER_DEBT.jsonl with decisions (category: enhancements)

Pre-Audit Setup

Step 0: Create Audit Directory

AUDIT_DIR="docs/audits/single-session/enhancements/audit-$(date +%Y-%m-%d)"
mkdir -p "$AUDIT_DIR"

CRITICAL: Verify AUDIT_DIR exists before EVERY agent spawn.

Step 1: Check State for Resume

Check for existing audit state:

# Check file-based state (source of truth)
ls ${AUDIT_DIR}/audit-state.json 2>/dev/null

If prior state found, offer to resume from last checkpoint.

Step 2: Gather Baselines

npm test 2>&1 | grep -E "Tests:|passing|failed" | head -5
npm run lint 2>&1 | tail -10
npm run patterns:check 2>&1
grep -E '"(next|react|typescript)"' package.json | head -5

Step 3: Load False Positives

# Read false positives to avoid re-flagging known issues
cat docs/technical-debt/FALSE_POSITIVES.jsonl 2>/dev/null

Step 4: Load TDMS Cross-Reference Data

# Load existing debt items for cross-referencing
wc -l docs/technical-debt/MASTER_DEBT.jsonl

Phase 1: Broad Scan — 8 Parallel Agents

CRITICAL: Agent Type Selection — Use subagent_type: "general-purpose" (NOT Explore). Explore agents are READ-ONLY and cannot write the required JSONL output files. All Phase 1 agents must write findings to disk.

Invoke all 8 agents in a SINGLE Task message. Each agent writes findings to ${AUDIT_DIR}/stage-1-{domain}.jsonl.

Agent 1: App Architecture Scanner

Focus: Component patterns, state management, data flow, code organization Files: app/, components/, lib/, hooks/, types/ Categories: app-architecture

Look for:

• Consolidation opportunities (duplicate patterns, similar components)
• State management improvements (context vs. global store vs. URL state)
• Data flow simplification (prop drilling, unnecessary lifting)
• Code organization improvements (colocation, barrel exports, index files)
• Pattern consistency across the codebase

Agent 2: Product & UX Scanner

Focus: User journeys, accessibility, interaction patterns, responsive design Files: app/, components/, styles/, public/ Categories: product-ux Method: Heuristic analysis

Look for:

• Accessibility gaps (ARIA labels, keyboard navigation, focus management)
• User journey friction points (extra clicks, unclear CTAs, dead ends)
• Responsive design issues (mobile breakpoints, touch targets)
• Loading state improvements (skeleton screens, optimistic updates)
• Error recovery UX (what happens when things fail)
• Recovery-domain sensitivity: Flag any findings that touch sensitive content areas but do NOT deep-dive into the therapeutic content itself

Agent 3: Content Scanner

Focus: Microcopy, button labels, error messages, onboarding text, help text Files: app/, components/ (string literals, i18n files) Categories: content

Look for:

• Unclear or generic button labels ("Submit" → more specific)
• Error messages that don't help the user fix the problem
• Missing or unhelpful empty states
• Onboarding flow copy improvements
• Consistency in terminology
• Recovery-domain terminology appropriateness (flag but don't rewrite)

Agent 4: DevX & Automation Scanner

Focus: Scripts, hooks, skills, commands, CI/CD, pre-commit pipeline Files: scripts/, .claude/, .github/, .husky/, package.json Categories: devx-automation

Look for:

• Script consolidation opportunities
• Pre-commit pipeline optimization (parallel execution, caching)
• Missing automation (manual steps that could be scripted)
• Skill gaps (common workflows without skill support)
• CI/CD improvements

Agent 5: Infrastructure & Stack Scanner

Focus: Firebase config, build config, dependency choices, deployment Files: firebase.json, next.config.*, package.json, functions/ Categories: infrastructure

Look for:

• Dependency update opportunities (major version bumps with benefits)
• Build optimization (bundle size, tree shaking, code splitting)
• Firebase configuration improvements (security rules, indexes)
• Next.js config optimization (image optimization, caching headers)

Agent 6: Testing Strategy Scanner

Focus: Test patterns, coverage approach, maintenance burden, ROI Files: tests/, __tests__/, *.test.*, test configs Categories: testing-strategy

Look for:

• Missing test categories (integration, e2e, visual regression)
• Test maintenance burden (brittle tests, over-mocking)
• Coverage gaps in critical paths
• Test performance improvements
• Testing strategy alignment with architecture

Agent 7: Documentation Strategy Scanner

Focus: Doc organization, freshness, usefulness, AI-workflow effectiveness Files: docs/, *.md, CLAUDE.md, AI_WORKFLOW.md Categories: documentation-strategy

Look for:

• Documentation that is read but rarely useful
• Missing documentation for common workflows
• Stale docs that cause confusion
• AI-workflow doc effectiveness (do the AI instructions actually help?)
• Documentation organization improvements

Agent 8: Workflow & External Scanner

Focus: Full dev lifecycle, session patterns, external service config Files: Session hooks, external configs, deployment scripts Categories: workflow-lifecycle, external-services

Look for:

• Friction points in: session start → coding → testing → commit → deploy
• External service optimization (Firebase, Sentry, analytics)
• Session workflow improvements
• Deployment pipeline enhancements

Phase 1 Agent Prompt Template

Each agent receives this base prompt (with domain-specific additions):

You are an Enhancement Auditor for the {DOMAIN} domain.

YOUR ROLE: Find things that could be BETTER, not things that are WRONG.
If an area is working well, say so explicitly — this becomes the Strengths section.

HONESTY REQUIREMENT: For every finding, you MUST include a counter_argument
explaining why NOT to make this change. If you can't think of one, the finding
is suspect — reconsider it.

CONFIDENCE REQUIREMENT: Only include findings where your confidence is 70% or above.
Below 70%, add to inconclusive section instead.

EVIDENCE REQUIREMENT: Every finding needs a concrete file path and specific indicator.

FILE PATH REQUIREMENT: The "files" array MUST contain real file paths from the codebase
(e.g., "components/ui/dialog.tsx:45"). NEVER use placeholders like "multiple", "various",
"1", or numeric values. If a finding affects multiple files, list ALL affected files
explicitly (up to 5). If there are more than 5, list the top 5 most relevant.
The TDMS intake pipeline will REJECT items with invalid file paths.

OUTPUT FORMAT: Write findings to ${AUDIT_DIR}/stage-1-{domain}.jsonl
One JSON object per line, with these fields:
- category, title, fingerprint, severity (S0-S3, see calibration below), effort (E0-E3), confidence (0-100)
- files (array of actual file:line refs — NO placeholders, NO numbers-only, NO "multiple")
- current_approach (what exists now and why)
- proposed_outcome (what the improved version looks like)
- counter_argument (REQUIRED: why NOT to do this)
- why_it_matters (the benefit)
- evidence (what you found)

ALSO OUTPUT: A "strengths" section at the end of your findings:
{"type": "strength", "domain": "{DOMAIN}", "description": "What's working well", "evidence": ["..."]}

WRITE ALL OUTPUT TO FILES. Never rely on conversation context.

**CRITICAL RETURN PROTOCOL:**
- Write findings to the specified output file using Write tool or Bash
- Return ONLY: `COMPLETE: [agent-id] wrote N findings to [path]`
- Do NOT return findings content in your response

Severity Scale Calibration

Use these codebase-specific examples to calibrate severity ratings. Agents tend to over-rate severity — use these as anchors:

Rule of thumb: If the change affects a single file or a small utility, it's S2 or S3. S0 is reserved for changes that would require a new milestone or architectural rethink. Most findings should be S1 or S2.

Legacy mapping: Previous versions used I0-I3 (Impact scale). If processing older enhancement findings: I0→S0, I1→S1, I2→S2, I3→S3.

Phase 1 Verification

After all 8 agents complete:

# Verify all output files exist and are non-empty
for domain in architecture ux content devx infrastructure testing docs workflow; do
  file="${AUDIT_DIR}/stage-1-${domain}.jsonl"
  if [ ! -s "$file" ]; then
    echo "MISSING OR EMPTY: $file — re-run agent"
  fi
done

Re-run any failed agents before proceeding.

Phase 2: Deep Dive — Adaptive Agents (Opt-In)

This phase is opt-in. After Phase 1 completes, present results to the user before spawning any deep-dive agents:

1. Summarize Phase 1 results: Show finding count by domain and impact tier
2. Identify clusters: Group findings into 5-8 opportunity clusters
3. Ask the user: "Phase 1 found N findings across M domains. Options:
   • Run Phase 2 deep-dive agents on the top clusters
   • Run /deep-research on top clusters first (evidence-based best practices from external sources), then deep-dive with research context
   • Skip to Phase 3 synthesis with Phase 1 findings only"
4. If deep-research: Run /deep-research on the top 2-3 clusters, then spawn deep-dive agents with research claims as context
5. If deep-dive only: Spawn deep-dive agents per cluster
6. If user skips: Proceed directly to Phase 3 with Phase 1 findings only

Deep-dive agents produce full-schema findings with:

• current_approach (what exists now, with code examples if helpful)
• proposed_outcome (concrete description of the improved version)
• counter_argument (genuine reasons NOT to do this)
• concrete_alternatives (specific tools, patterns, libraries by name)
• implementation_notes (guidance for Claude Code on how to implement)
• acceptance_tests (how to verify the improvement worked)
• risk_assessment (what could go wrong)
• Web search for competitor benchmarking where relevant

Meta-improvements: At least one deep-dive agent evaluates the audit/skill system itself (meta-tooling category).

Output: ${AUDIT_DIR}/stage-2-{cluster-name}.jsonl

Phase 3: Synthesis

Sequential process (1-2 agents):

1. Merge: Combine all stage-1 and stage-2 JSONL files

2. Ingest: Run node scripts/debt/intake-audit.js ${AUDIT_DIR}/merged-all.jsonl --source "audit-enhancements-YYYY-MM-DD"

   • This automatically detects enhancement format, maps fields, runs dedup and generates views
   • Items ingested as category: "enhancements", type: "enhancement" in TDMS

3. Cross-reference: Intake script automatically handles TDMS dedup

4. Confidence filter: Findings below 70% → "Inconclusive" section

5. Honesty check: Verify all findings have non-empty counter_argument

6. Strengths compilation: Gather all type: "strength" entries from Phase 1

7. Generate Report: Write ${AUDIT_DIR}/ENHANCEMENT_AUDIT_REPORT.md:

# Enhancement Audit Report — YYYY-MM-DD

## Executive Summary

- X findings across Y categories
- Z strengths identified (areas working well)
- Top 3 highest-impact opportunities

## Strengths (What's Working Well)

[Areas evaluated and found adequate, organized by domain]

## Findings by Impact

### S0 — Critical / Transformative (N findings)

[Full finding details]

### S1 — High / Significant (N findings)

[...]

### S2 — Medium / Moderate (N findings)

[...]

### S3 — Low / Minor (N findings)

[...]

## Inconclusive (Confidence < 70%)

[Findings that need more investigation]

## TDMS Cross-References

[Improvements that relate to existing debt items]

## Meta-Improvements

[Findings about the audit/skill system itself]

Phase 4: Interactive Review & TDMS Intake

Read .claude/skills/_shared/AUDIT_TEMPLATE.md for: MASTER_DEBT Cross-Reference, Interactive Review, and TDMS Intake procedures.

Enhancement-specific additions to interactive review:

• Use resolve-item.js for each decision: node scripts/debt/resolve-item.js DEBT-XXXX --action {accept|decline|defer} --reason "{user's reason}"
• Save state to ${AUDIT_DIR}/audit-state.json after each batch

Post-Review: Roadmap Placement

After ALL findings have been reviewed (not per-item), present accepted items as a batch for roadmap placement:

1. List all ACCEPTED items with their impact/effort ratings
2. Propose placement in ROADMAP.md sections based on effort and category:
   • E0 items --> current Quick Wins milestone
   • E1 items --> current or next milestone
   • E2+ items --> appropriate future milestone
3. Get user confirmation on all placements at once
4. Apply roadmap edits in a single pass
5. Check for duplicate findings among accepted items and offer to merge

State Persistence

Source of truth: ${AUDIT_DIR}/audit-state.json — tracks current_phase, completed/pending agents per phase, review progress, and decisions. Update after every phase transition.

Enhancement-Specific Guardrails

In addition to the shared Honesty Guardrails (see AUDIT_TEMPLATE.md):

• No-change validation: Phase 1 agents MUST explicitly list areas they evaluated and found adequate. These become the "Strengths" section.
• Consolidation bias guard: Evaluate over/under-consolidation on merit.
• Semantic dedup: Merge findings with different fingerprints but similar proposed fixes.

Post-Audit

1. Save final state to ${AUDIT_DIR}/audit-state.json
2. Display summary to user with link to ENHANCEMENT_AUDIT_REPORT.md

Pre-Commit Compatibility

Enhancement audit JSONL files use a different schema than strict audit JSONL files. When committing audit artifacts, use:

SKIP_AUDIT_VALIDATION=1 git commit -m "audit(enhancements): ..."

TDMS Integration

Skill-specific TDMS intake:

node scripts/debt/intake-audit.js <findings.jsonl> --source "audit-enhancements-YYYY-MM-DD"

Items ingested as category: "enhancements", type: "enhancement" in TDMS.

Multi-AI Template

For cross-model consensus audits, see: docs/audits/multi-ai/templates/ENHANCEMENT_AUDIT.md

This template can be injected into any AI system (Claude, GPT, Gemini, Copilot) with the repo context for independent enhancement discovery.

Version History