When to Use This Skill

Use this skill when:

• Code quality issues are detected (violations, smells, anti-patterns)
• Audit requirements mandate systematic review (compliance, release gates)
• Review needs arise (pre-merge, production hardening, refactoring preparation)
• Quality metrics indicate degradation (test coverage drop, complexity increase)
• Theater detection is needed (mock data, stubs, incomplete implementations)

When NOT to Use This Skill

Do NOT use this skill for:

• Simple formatting fixes (use linter/prettier directly)
• Non-code files (documentation, configuration without logic)
• Trivial changes (typo fixes, comment updates)
• Generated code (build artifacts, vendor dependencies)
• Third-party libraries (focus on application code)

Success Criteria

This skill succeeds when:

• Violations Detected: All quality issues found with ZERO false negatives
• False Positive Rate: <5% (95%+ findings are genuine issues)
• Actionable Feedback: Every finding includes file path, line number, and fix guidance
• Root Cause Identified: Issues traced to underlying causes, not just symptoms
• Fix Verification: Proposed fixes validated against codebase constraints

Edge Cases and Limitations

Handle these edge cases carefully:

• Empty Files: May trigger false positives - verify intent (stub vs intentional)
• Generated Code: Skip or flag as low priority (auto-generated files)
• Third-Party Libraries: Exclude from analysis (vendor/, node_modules/)
• Domain-Specific Patterns: What looks like violation may be intentional (DSLs)
• Legacy Code: Balance ideal standards with pragmatic technical debt management

Quality Analysis Guardrails

CRITICAL RULES - ALWAYS FOLLOW:

• NEVER approve code without evidence: Require actual execution, not assumptions
• ALWAYS provide line numbers: Every finding MUST include file:line reference
• VALIDATE findings against multiple perspectives: Cross-check with complementary tools
• DISTINGUISH symptoms from root causes: Report underlying issues, not just manifestations
• AVOID false confidence: Flag uncertain findings as "needs manual review"
• PRESERVE context: Show surrounding code (5 lines before/after minimum)
• TRACK false positives: Learn from mistakes to improve detection accuracy

Evidence-Based Validation

Use multiple validation perspectives:

1. Static Analysis: Code structure, patterns, metrics (connascence, complexity)
2. Dynamic Analysis: Execution behavior, test results, runtime characteristics
3. Historical Analysis: Git history, past bug patterns, change frequency
4. Peer Review: Cross-validation with other quality skills (functionality-audit, theater-detection)
5. Domain Expertise: Leverage .claude/expertise/{domain}.yaml if available

Validation Threshold: Findings require 2+ confirming signals before flagging as violations.

Integration with Quality Pipeline

This skill integrates with:

• Pre-Phase: Load domain expertise (.claude/expertise/{domain}.yaml)
• Parallel Skills: functionality-audit, theater-detection-audit, style-audit
• Post-Phase: Store findings in Memory MCP with WHO/WHEN/PROJECT/WHY tags
• Feedback Loop: Learnings feed dogfooding-system for continuous improvement

Quick Quality Check

Purpose

Run a fast, comprehensive quality check on code in under 30 seconds using parallel execution.

Specialist Agent

I am a quality assurance specialist focused on rapid feedback loops.

Methodology (Parallel Execution Pattern):

1. Spawn swarm with optimal topology for speed
2. Execute independent checks in parallel
3. Aggregate results in real-time
4. Provide instant actionable feedback
5. Prioritize findings by severity

Checks Performed (parallel):

• Theater detection (mocks, TODOs, placeholders)
• Style audit (linting, formatting)
• Security scan (vulnerabilities, unsafe patterns)
• Basic test execution
• Token usage analysis

Output: Unified quality report with severity-ranked issues

Input Contract

input:
  path: string (file or directory path, required)
  parallel: boolean (default: true)
  quick_mode: boolean (skip deep analysis, default: true)

Output Contract

output:
  quality_score: number (0-100)
  issues:
    critical: array[issue]
    high: array[issue]
    medium: array[issue]
    low: array[issue]
  execution_time: number (seconds)
  checks_run: array[string]

Execution Flow

# Initialize swarm for parallel execution
npx claude-flow coordination swarm-init --topology mesh --max-agents 5

# Spawn specialized agents in parallel
npx claude-flow automation auto-agent --task "Quick quality assessment" --strategy optimal

# Execute all checks in parallel
parallel ::: \
  "npx claude-flow theater-detect '$path' --output theater.json" \
  "npx claude-flow style-audit '$path' --quick --output style.json" \
  "npx claude-flow security-scan '$path' --fast --output security.json" \
  "npx claude-flow test-coverage '$path' --quick --output tests.json" \
  "npx claude-flow analysis token-usage --time-range 1h --output tokens.json"

# Aggregate results
npx claude-flow merge-reports theater.json style.json security.json tests.json tokens.json \
  --output quality-report.json \
  --prioritize severity

# Display summary
cat quality-report.json | jq '.summary'

Integration Points

Cascades

• Part of /production-readiness cascade
• Used by /code-review-assistant cascade
• Invoked by /quick-check command

Commands

• Combines: /theater-detect, /style-audit, /security-scan, /test-coverage, /token-usage
• Uses: /swarm-init, /auto-agent, /parallel-execute

Other Skills

• Input to deep-code-audit skill
• Used by pre-commit-check skill
• Part of continuous-quality skill

Usage Example

# Quick check current directory
quick-quality-check .

# Quick check specific file
quick-quality-check src/api/users.js

# Quick check with detailed output
quick-quality-check src/ --detailed

Failure Modes

• Insufficient resources: Reduce parallelism, run sequentially
• Tests failing: Flag but continue other checks
• Security issues found: Escalate to detailed security review
• Poor quality score: Trigger deep-code-audit skill