codebase-cleanup-tech-debt
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 acti
Best use case
codebase-cleanup-tech-debt is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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 acti
Teams using codebase-cleanup-tech-debt should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/codebase-cleanup-tech-debt/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How codebase-cleanup-tech-debt Compares
| Feature / Agent | codebase-cleanup-tech-debt | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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 acti
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
$ARGUMENTS
## 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**
```yaml
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
outdated_minor: 34
security_vulnerabilities: 7
deprecated_apis: 15
```
**Trend Analysis**
```python
debt_trends = {
"2024_Q1": {"score": 750, "items": 125},
"2024_Q2": {"score": 820, "items": 142},
"2024_Q3": {"score": 890, "items": 156},
"growth_rate": "18% quarterly",
"projection": "1200 by 2025_Q1 without intervention"
}
```
### 4. Prioritized Remediation Plan
Create an actionable roadmap based on ROI:
**Quick Wins (High Value, Low Effort)**
Week 1-2:
```
1. Extract duplicate validation logic to shared module
Effort: 8 hours
Savings: 20 hours/month
ROI: 250% in first month
2. Add error monitoring to payment service
Effort: 4 hours
Savings: 15 hours/month debugging
ROI: 375% in first month
3. Automate deployment script
Effort: 12 hours
Savings: 2 hours/deployment × 20 deploys/month
ROI: 333% in first month
```
**Medium-Term Improvements (Month 1-3)**
```
1. Refactor OrderService (God class)
- Split into 4 focused services
- Add comprehensive tests
- Create clear interfaces
Effort: 60 hours
Savings: 30 hours/month maintenance
ROI: Positive after 2 months
2. Upgrade React 16 → 18
- Update component patterns
- Migrate to hooks
- Fix breaking changes
Effort: 80 hours
Benefits: Performance +30%, Better DX
ROI: Positive after 3 months
```
**Long-Term Initiatives (Quarter 2-4)**
```
1. Implement Domain-Driven Design
- Define bounded contexts
- Create domain models
- Establish clear boundaries
Effort: 200 hours
Benefits: 50% reduction in coupling
ROI: Positive after 6 months
2. Comprehensive Test Suite
- Unit: 80% coverage
- Integration: 60% coverage
- E2E: Critical paths
Effort: 300 hours
Benefits: 70% reduction in bugs
ROI: Positive after 4 months
```
### 5. Implementation Strategy
**Incremental Refactoring**
```python
# Phase 1: Add facade over legacy code
class PaymentFacade:
def __init__(self):
self.legacy_processor = LegacyPaymentProcessor()
def process_payment(self, order):
# New clean interface
return self.legacy_processor.doPayment(order.to_legacy())
# Phase 2: Implement new service alongside
class PaymentService:
def process_payment(self, order):
# Clean implementation
pass
# Phase 3: Gradual migration
class PaymentFacade:
def __init__(self):
self.new_service = PaymentService()
self.legacy = LegacyPaymentProcessor()
def process_payment(self, order):
if feature_flag("use_new_payment"):
return self.new_service.process_payment(order)
return self.legacy.doPayment(order.to_legacy())
```
**Team Allocation**
```yaml
Debt_Reduction_Team:
dedicated_time: "20% sprint capacity"
roles:
- tech_lead: "Architecture decisions"
- senior_dev: "Complex refactoring"
- dev: "Testing and documentation"
sprint_goals:
- sprint_1: "Quick wins completed"
- sprint_2: "God class refactoring started"
- sprint_3: "Test coverage >60%"
```
### 6. Prevention Strategy
Implement gates to prevent new debt:
**Automated Quality Gates**
```yaml
pre_commit_hooks:
- complexity_check: "max 10"
- duplication_check: "max 5%"
- test_coverage: "min 80% for new code"
ci_pipeline:
- dependency_audit: "no high vulnerabilities"
- performance_test: "no regression >10%"
- architecture_check: "no new violations"
code_review:
- requires_two_approvals: true
- must_include_tests: true
- documentation_required: true
```
**Debt Budget**
```python
debt_budget = {
"allowed_monthly_increase": "2%",
"mandatory_reduction": "5% per quarter",
"tracking": {
"complexity": "sonarqube",
"dependencies": "dependabot",
"coverage": "codecov"
}
}
```
### 7. Communication Plan
**Stakeholder Reports**
```markdown
## Executive Summary
- Current debt score: 890 (High)
- Monthly velocity loss: 35%
- Bug rate increase: 45%
- Recommended investment: 500 hours
- Expected ROI: 280% over 12 months
## Key Risks
1. Payment system: 3 critical vulnerabilities
2. Data layer: No backup strategy
3. API: Rate limiting not implemented
## Proposed Actions
1. Immediate: Security patches (this week)
2. Short-term: Core refactoring (1 month)
3. Long-term: Architecture modernization (6 months)
```
**Developer Documentation**
```markdown
## Refactoring Guide
1. Always maintain backward compatibility
2. Write tests before refactoring
3. Use feature flags for gradual rollout
4. Document architectural decisions
5. Measure impact with metrics
## Code Standards
- Complexity limit: 10
- Method length: 20 lines
- Class length: 200 lines
- Test coverage: 80%
- Documentation: All public APIs
```
### 8. Success Metrics
Track progress with clear KPIs:
**Monthly Metrics**
- Debt score reduction: Target -5%
- New bug rate: Target -20%
- Deployment frequency: Target +50%
- Lead time: Target -30%
- Test coverage: Target +10%
**Quarterly Reviews**
- Architecture health score
- Developer satisfaction survey
- Performance benchmarks
- Security audit results
- Cost savings achieved
## Output Format
1. **Debt Inventory**: Comprehensive list categorized by type with metrics
2. **Impact Analysis**: Cost calculations and risk assessments
3. **Prioritized Roadmap**: Quarter-by-quarter plan with clear deliverables
4. **Quick Wins**: Immediate actions for this sprint
5. **Implementation Guide**: Step-by-step refactoring strategies
6. **Prevention Plan**: Processes to avoid accumulating new debt
7. **ROI Projections**: Expected returns on debt reduction investment
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