Agent skill

Code Refactoring Tech Debt

by sickn33 in sickn33/agentic-awesome-skills

Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions.

MITAuto-check passedDevelopment

Install Code Refactoring Tech Debt

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill code-refactoring-tech-debt -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills code-refactoring-tech-debt --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/code-refactoring-tech-debt .claude/skills/code-refactoring-tech-debt && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
code-refactoring-tech-debt
GitHub stars
47k
Used in
1 other repo
Token cost
~2.9k tokens
SKILL.md length
702 words
Files
1
Skills in repo
1,394
Repo updated
First seen
Licence
MIT

At a glance

Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions.

  • Works in 8 steps: Technical Debt Inventory → Impact Assessment → Debt Metrics Dashboard → …
  • Tasks that involve Technical debt
  • SKILL.md covers Compatibility and maintenance, Use this skill when, Do not use this skill when and Context, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Code Refactoring Tech Debt is an agent skill from sickn33/agentic-awesome-skills. Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Technical debt and Refactoring. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Technical debt
  • Tasks that involve Refactoring

Example prompts

  • “/code-refactoring-tech-debt”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Technical Debt Inventory
  2. Impact Assessment
  3. Debt Metrics Dashboard
  4. Prioritized Remediation Plan
  5. Implementation Strategy
  6. Prevention Strategy
  7. Communication Plan
  8. Success Metrics

What it can do on your machine

Read from SKILL.md and the folder at commit 1e53ce2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, python and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Code Refactoring Tech Debt loads about 2.9k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 702 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 1e53ce2, republished under its MIT licence (© sickn33). 702 words, ~2,870 tokens.

Download SKILL.mdSave it as .claude/skills/code-refactoring-tech-debt/SKILL.md (or your agent's skills folder).
name
code-refactoring-tech-debt
description
Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions.
risk
safe
source
community
date_added
2026-02-27

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
Show full SKILL.md (276 more words)Show less
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": "18.7% across Q1 to Q3 in this hypothetical series",
    "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
   Illustrative net time ROI: (20 - 8) / 8 = 150% in first month

2. Add error monitoring to payment service
   Effort: 4 hours
   Savings: 15 hours/month debugging
   Illustrative net time ROI: (15 - 4) / 4 = 275% in first month

3. Automate deployment script
   Effort: 12 hours
   Savings: 2 hours/deployment × 20 deploys/month
   Illustrative net time ROI: (40 - 12) / 12 = 233% 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: measure compatibility and actual performance; no assumed uplift
   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: assess actual coupling after a scoped change
   ROI: Positive after 6 months

2. Comprehensive Test Suite
   - Unit: 80% coverage
   - Integration: 60% coverage
   - E2E: Critical paths
   Effort: 300 hours
   Benefits: measure escaped defects; no guaranteed reduction
   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

Focus on delivering measurable improvements that directly impact development velocity, system reliability, and team morale.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/code-refactoring-tech-debt of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 1e53ce2

Used in 1 other repository

We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Tech Debt Analyzerailabs-393/ai-labs-claude-skills4542 repos~3.9kAutomated safety check: PassMIT
FIXME Resolvertailcallhq/forgecode7.6k—~1.1kAutomated safety check: PassApache-2.0

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Categories

Questions about Code Refactoring Tech Debt

What does Code Refactoring Tech Debt do?

Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions. Code Refactoring Tech Debt is an agent skill from sickn33/agentic-awesome-skills. Identify technical debt from actual code and change history, estimate its impact, and prioritize bounded improvements with explicit assumptions.

When should I use Code Refactoring Tech Debt?

Code Refactoring Tech Debt fits situations like: tasks that involve Technical debt; tasks that involve Refactoring.

How do I install Code Refactoring Tech Debt in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill code-refactoring-tech-debt -a claude-code`. Or copy the skill folder (skills/code-refactoring-tech-debt in sickn33/agentic-awesome-skills) into .claude/skills/code-refactoring-tech-debt in your project. Claude Code loads it when a task matches its description.

How do I install Code Refactoring Tech Debt in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill code-refactoring-tech-debt -a codex`. Or copy the skill folder (skills/code-refactoring-tech-debt in sickn33/agentic-awesome-skills) into .agents/skills/code-refactoring-tech-debt in your project. Codex loads it when a task matches its description.

Can I use Code Refactoring Tech Debt in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill code-refactoring-tech-debt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-refactoring-tech-debt, .gemini/skills/code-refactoring-tech-debt, .github/skills/code-refactoring-tech-debt and .opencode/skills/code-refactoring-tech-debt in your project.

What does Code Refactoring Tech Debt need to run?

SKILL.md names no scripts, command-line tools or credentials: Code Refactoring Tech Debt is instructions for the agent only. Our summary lists: Python 3.

Does Code Refactoring Tech Debt access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Code Refactoring Tech Debt safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Code Refactoring Tech Debt use?

Code Refactoring Tech Debt is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Refactoring Tech Debt use?

About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Code Refactoring Tech Debt?

Skills that share tags, products or a category with Code Refactoring Tech Debt: Systematic Code Refactoring (luongnv89/claude-howto, 42k stars), Code Simplification for ego-lite (citrolabs/ego-lite, 17k stars), Code Refactoring Workflow (luongnv89/claude-howto, 42k stars) and Tech Debt Analyzer (ailabs-393/ai-labs-claude-skills, 454 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Refactoring Tech Debt?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,304 GitHub stars. The repository holds 1,394 skills in this directory. The repository was last updated on October 6, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.