Single-Session Code Review Audit

When to Use

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

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

Execution Mode Selection

Section A: Parallel Architecture (3 Agents)

When to use: Task tool available, sufficient context budget

Agent 1: hygiene-and-types

Focus Areas:

• Code Hygiene (unused imports, dead code, console.logs)
• Types & Correctness (any types, type safety, null checks)

Files:

• app/**/*.tsx, components/**/*.tsx
• lib/**/*.ts, hooks/**/*.ts
• types/**/*.ts

Agent 2: framework-and-testing

Focus Areas:

• Framework Best Practices (React patterns, Next.js conventions)
• Testing Coverage (untested functions, missing edge cases)

Files:

• app/**/*.tsx (routing, layouts)
• components/**/*.tsx (component patterns)
• tests/**/*.test.ts

Agent 3: security-and-debugging

Focus Areas:

• Security Surface (input validation, auth checks)
• AI-Generated Code Failure Modes
• Debugging Ergonomics

Files:

• lib/auth*.ts, middleware.ts
• functions/src/**/*.ts
• Error handling code, logging patterns

Parallel Execution Command

Invoke all 3 agents in a SINGLE Task message:

Task 1: hygiene-and-types agent - audit code hygiene and TypeScript patterns
Task 2: framework-and-testing agent - audit React/Next.js patterns and test
coverage Task 3: security-and-debugging agent - audit security, AI patterns,
debugging

Each agent prompt MUST end with:

CRITICAL RETURN PROTOCOL:

- Write findings to the specified output file using Write tool or Bash
- Return ONLY: `COMPLETE: [agent-id] wrote N findings to [output-path]`
- Do NOT return full findings content — orchestrator checks completion via file

Dependency constraints: All 3 agents are independent -- no ordering required. Each writes to a separate JSONL section. Results are merged after all agents complete.

Coordination Rules

1. Each agent writes findings to separate JSONL section
2. Hygiene findings have lowest priority in conflicts
3. Security findings have highest priority
4. Framework agent handles boundary issues

Section B: Sequential Fallback (Single Agent)

When to use: Task tool unavailable, context limits, or user preference

Execution Order:

1. AICode Patterns (catches hallucinations early) - 15 min
2. Types & Correctness - 10 min
3. Testing Coverage - 10 min
4. Remaining categories - 15 min

Total: ~50 min (vs ~15 min parallel)

Checkpoint Format

{
  "started_at": "ISO timestamp",
  "categories_completed": ["Hygiene", "Types"],
  "current_category": "Framework",
  "findings_count": 18,
  "last_file_written": "stage-2-findings.jsonl"
}

Pre-Audit Validation

Step 0: Episodic Memory Search (Session #128)

Before running code audit, search for context from past code review sessions:

// Search for past code audit findings
mcp__plugin_episodic -
  memory_episodic -
  memory__search({
    query: ["code audit", "patterns", "quality"],
    limit: 5,
  });

// Search for AI-generated code issues addressed before
mcp__plugin_episodic -
  memory_episodic -
  memory__search({
    query: ["AICode", "hallucinated", "dead code"],
    limit: 5,
  });

Why this matters:

• Compare against previous code quality findings
• Identify recurring anti-patterns (may indicate architectural debt)
• Track which issues were resolved vs regressed
• Prevent re-flagging known patterns

Step 1: Check Thresholds

Run npm run review:check and report results. If no thresholds are triggered:

• Display: "⚠️ No review thresholds triggered. Proceed anyway? (This is a lightweight single-session audit)"
• Continue with audit regardless (user invoked intentionally)

Step 2: Gather Current Baselines

Collect these metrics by running commands:

# Test count
npm test 2>&1 | grep -E "Tests:|passing|failed" | head -5

# Lint status
npm run lint 2>&1 | tail -10

# Pattern compliance
npm run patterns:check 2>&1

# Stack versions
grep -E '"(next|react|typescript)"' package.json | head -5

Step 2b: Query SonarCloud (if MCP available)

If mcp__sonarcloud__get_issues is available, fetch current issue counts:

• Query with types: "CODE_SMELL,BUG" and severities: "CRITICAL,MAJOR"
• Compare against baseline from previous SonarCloud sync (778 issues as of 2026-01-05)
• Note any significant changes (>10% increase/decrease)

This provides real-time issue data to cross-reference with audit findings.

Step 3: Load False Positives Database

Read docs/technical-debt/FALSE_POSITIVES.jsonl and filter findings matching:

• Category: code
• Expired entries (skip if expires date passed)

Note patterns to exclude from final findings.

Step 4: Check Template Currency

Read docs/audits/multi-ai/templates/CODE_REVIEW_PLAN.md and verify:

• Stack versions match package.json
• Test count baseline is accurate
• File paths in scope still exist
• Review range in AI_REVIEW_LEARNINGS_LOG.md is current

If outdated, note discrepancies but proceed with current values.

Audit Execution

Focus Areas (7 Categories):

1. Code Hygiene (unused imports, dead code, console.logs)
2. Types & Correctness (any types, type safety, null checks)
3. Framework Best Practices (React patterns, Next.js conventions)
4. Testing Coverage (untested functions, missing edge cases)
5. Security Surface (input validation, auth checks)
6. AICode (AI-Generated Code Failure Modes):
   • "Happy-path only" logic, missing edge cases and error handling (S1)
   • Tests that exist but don't assert meaningful behavior (S1)
   • Hallucinated dependencies/APIs that don't exist (S1)
   • Copy/paste anti-patterns (similar code blocks that should be abstracted) (S2)
   • Inconsistent architecture patterns across files (S2)
   • Overly complex functions (deep nesting, >50 lines) (S2)
   • Session Boundary Inconsistencies: Conflicting patterns from different AI sessions (S2)
   • Dead Code from Iterations: Commented code, unused variables from AI iterations (S3)
   • AI TODO Markers: "TODO: AI should fix this", "FIXME: Claude" patterns (S3)
   • Over-Engineering: Unnecessary abstractions, premature optimization (S2)
7. Debugging (Debugging Ergonomics) (NEW - 2026-01-13):
   • Correlation IDs / request tracing (frontend to backend)
   • Structured logging with context (not just console.log)
   • Sentry/error tracking integration completeness
   • Error messages include actionable fix hints
   • Offline/network status captured in error context

For each category:

1. Search relevant files using Grep/Glob
2. Identify specific issues with file:line references
3. Classify severity: S0 (Critical) | S1 (High) | S2 (Medium) | S3 (Low)
4. Estimate effort: E0 (trivial) | E1 (hours) | E2 (day) | E3 (major)
5. Assign confidence level (see Evidence Requirements below)

Category Token Requirement (MANDATORY):

• In JSONL output, category MUST be one of: Hygiene|Types|Framework|Testing|Security|AICode|Debugging
• Do NOT include spaces, parentheses, or descriptive suffixes (e.g., output AICode, not AICode (AI-Generated Code Failure Modes))

AI-Code Specific Checks:

• Functions with only happy-path logic (no try/catch, no null checks)
• Test files with expect(true).toBe(true) or trivial assertions
• Import statements for packages not in package.json
• Multiple similar code blocks (>10 lines duplicated)
• Functions with >3 levels of nesting

Scope:

• Include: app/, components/, lib/, hooks/, types/
• Exclude: node_modules/, .next/, docs/
• Conditional: tests/ excluded for code hygiene, but included when analyzing Testing Coverage (category 4) and AI-Generated Code (category 6)

Output Requirements

1. Markdown Summary (display to user):

## Code Review Audit - [DATE]

### Baselines

- Tests: X passing, Y failing
- Lint: X errors, Y warnings
- Patterns: X violations

### Findings Summary

| Severity | Count | Top Issues | Confidence  |
| -------- | ----- | ---------- | ----------- |
| S0       | X     | ...        | HIGH/MEDIUM |
| S1       | X     | ...        | HIGH/MEDIUM |
| S2       | X     | ...        | ...         |
| S3       | X     | ...        | ...         |

### Top 5 Issues

1. [file:line] - Description (S1/E1) - DUAL_PASS_CONFIRMED
2. ...

### False Positives Filtered

- X findings excluded (matched FALSE_POSITIVES.jsonl patterns)

### Quick Wins (E0-E1)

- ...

### Recommendations

- ...

2. JSONL Findings (save to file):

Create file: docs/audits/single-session/code/audit-[YYYY-MM-DD].jsonl

Category field: category MUST be code-quality

3. Markdown Report (save to file):

Create file: docs/audits/single-session/code/audit-[YYYY-MM-DD].md

Full markdown report with all findings, baselines, and recommendations.

Standard Audit Procedures

Read .claude/skills/_shared/AUDIT_TEMPLATE.md for: Evidence Requirements, Dual-Pass Verification, Cross-Reference Validation, JSONL Output Format, Context Recovery, Post-Audit Validation, MASTER_DEBT Cross-Reference, Interactive Review, TDMS Intake & Commit, Documentation References, Agent Return Protocol, and Honesty Guardrails.

Skill-specific TDMS intake:

node scripts/debt/intake-audit.js <output.jsonl> --source "audit-code-<date>"

Code audit triggers (check AUDIT_TRACKER.md):

• 25+ commits since last code audit, OR
• 15+ code files modified since last code audit

Version History