Compatibility and maintenance

Compatibility alias of code-refactoring-tech-debt; use that ID for new references when no existing contract requires this one. The full instructions and support files remain local so existing installations continue to work offline. This is one shared procedure, not an additional capability. Preserve the callable ID when an existing manifest or client configuration uses it. Modified in AAS on 2026-09-05; original metadata and license notices are retained.

Technical Debt Analysis and Remediation

You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create actionable remediation plans.

Use this skill when

• Working on technical debt analysis and remediation tasks or workflows
• Needing guidance, best practices, or checklists for technical debt analysis and remediation

Do not use this skill when

• The task is unrelated to technical debt analysis and remediation
• You need a different domain or tool outside this scope

Context

The user needs a comprehensive technical debt analysis to understand what's slowing down development, increasing bugs, and creating maintenance challenges. Focus on practical, measurable improvements with clear ROI.

Requirements

Inspect the current repository, hotspots and real change/incident history. The numbers, thresholds, staffing and timelines below are hypothetical planning examples, not measurements, promised returns or mandatory quality gates. Report missing cost/usage inputs as unknown; never fill them with fabricated telemetry. Review-only scope does not authorize broad refactors, policy changes or deployment.

Instructions

1. Technical Debt Inventory

Conduct a thorough scan for all types of technical debt:

Code Debt

• Duplicated Code
• Exact duplicates (copy-paste)
• Similar logic patterns
• Repeated business rules
• Quantify: Lines duplicated, locations

• Complex Code
• High cyclomatic complexity (>10)
• Deeply nested conditionals (>3 levels)
• Long methods (>50 lines)
• God classes (>500 lines, >20 methods)
• Quantify: Complexity scores, hotspots

• Poor Structure
• Circular dependencies
• Inappropriate intimacy between classes
• Feature envy (methods using other class data)
• Shotgun surgery patterns
• Quantify: Coupling metrics, change frequency

Architecture Debt

• Design Flaws
• Missing abstractions
• Leaky abstractions
• Violated architectural boundaries
• Monolithic components
• Quantify: Component size, dependency violations

• Technology Debt
• Outdated frameworks/libraries
• Deprecated API usage
• Legacy patterns (e.g., callbacks vs promises)
• Unsupported dependencies
• Quantify: Version lag, security vulnerabilities

Testing Debt

• Coverage Gaps
• Untested code paths
• Missing edge cases
• No integration tests
• Lack of performance tests
• Quantify: Coverage %, critical paths untested

• Test Quality
• Brittle tests (environment-dependent)
• Slow test suites
• Flaky tests
• No test documentation
• Quantify: Test runtime, failure rate

Documentation Debt

• Missing Documentation
• No API documentation
• Undocumented complex logic
• Missing architecture diagrams
• No onboarding guides
• Quantify: Undocumented public APIs

Infrastructure Debt

• Deployment Issues
• Manual deployment steps
• No rollback procedures
• Missing monitoring
• No performance baselines
• Quantify: Deployment time, failure rate

2. Impact Assessment

Calculate the real cost of each debt item:

Development Velocity Impact

Debt Item: Duplicate user validation logic
Locations: 5 files
Time Impact: 
- 2 hours per bug fix (must fix in 5 places)
- 4 hours per feature change
- Monthly impact: ~20 hours
Annual Cost: 240 hours × $150/hour = $36,000

Quality Impact

Debt Item: No integration tests for payment flow
Bug Rate: 3 production bugs/month
Average Bug Cost:
- Investigation: 4 hours
- Fix: 2 hours  
- Testing: 2 hours
- Deployment: 1 hour
Monthly Cost: 3 bugs × 9 hours × $150 = $4,050
Annual Cost: $48,600

Risk Assessment

• Critical: Security vulnerabilities, data loss risk
• High: Performance degradation, frequent outages
• Medium: Developer frustration, slow feature delivery
• Low: Code style issues, minor inefficiencies

3. Debt Metrics Dashboard

Create measurable KPIs:

Code Quality Metrics

Metrics:
  cyclomatic_complexity:
    current: 15.2
    target: 10.0
    files_above_threshold: 45
    
  code_duplication:
    percentage: 23%
    target: 5%
    duplication_hotspots:
      - src/validation: 850 lines
      - src/api/handlers: 620 lines
      
  test_coverage:
    unit: 45%
    integration: 12%
    e2e: 5%
    target: 80% / 60% / 30%
    
  dependency_health:
    outdated_major: 12