code-refactoring-tech-debt
Helps your agent find and prioritize improvements in old or problematic code. It analyzes history and code to suggest fixes that bring more benefits.
What it does
This tool analyzes your code to find issues that can slow down development or cause bugs. It suggests which problems to fix first based on their impact and the effort required.
When to use it
- ✓When code is hard to understand or modify.
- ✓When many bugs appear in specific code areas.
- ✓Before making major changes to avoid introducing new problems.
Example of what to ask your AI
“Analyze my code and tell me the biggest issues and how to fix them first.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool examines the code and its change history to identify duplicated, complex, or outdated parts. It then estimates the cost of these issues and suggests a plan to fix them, starting with the most important ones.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill code-refactoring-tech-debtOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/code-refactoring-tech-debt/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Compatibility and maintenance
Primary editorial path for this compatibility group. 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:
…
FAQ
What is 'technical debt'?
It's like financial debt, but in code. It refers to shortcuts or quick fixes that, over time, make software harder and more expensive to maintain and evolve.
What kinds of problems does it find?
It finds duplicated code, overly complex code, lack of tests, outdated libraries, architectural issues, and incomplete documentation.
How does it decide what to fix first?
It prioritizes issues that cause the most development slowdowns or bugs, and that are easier to fix, focusing on a good return on investment.
Details
Original description by the author
Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2571
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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