tech-debt

Donchitos/Claude-Code-Game-Studios · updated May 19, 2026

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$npx skills add https://github.com/Donchitos/Claude-Code-Game-Studios --skill tech-debt
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summary

### Tech Debt

  • description: "Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment scheduling."
  • argument-hint: "[scan|add|prioritize|report]"
  • allowed-tools: Read, Glob, Grep, Write
skill.md
name
tech-debt
description
"Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment scheduling."
argument-hint
"[scan|add|prioritize|report]"
user-invocable
true
allowed-tools
Read, Glob, Grep, Write

Phase 1: Parse Subcommand

Determine the mode from the argument:

  • scan — Scan the codebase for tech debt indicators
  • add — Add a new tech debt entry manually
  • prioritize — Re-prioritize the existing debt register
  • report — Generate a summary report of current debt status

If no subcommand is provided, output usage and stop. Verdict: FAIL — missing required subcommand.


Phase 2A: Scan Mode

Search the codebase for debt indicators:

  • TODO comments (count and categorize)
  • FIXME comments (these are bugs disguised as debt)
  • HACK comments (workarounds that need proper solutions)
  • @deprecated markers
  • Duplicated code blocks (similar patterns in multiple files)
  • Files over 500 lines (potential god objects)
  • Functions over 50 lines (potential complexity)

Categorize each finding:

  • Architecture Debt: Wrong abstractions, missing patterns, coupling issues
  • Code Quality Debt: Duplication, complexity, naming, missing types
  • Test Debt: Missing tests, flaky tests, untested edge cases
  • Documentation Debt: Missing docs, outdated docs, undocumented APIs
  • Dependency Debt: Outdated packages, deprecated APIs, version conflicts
  • Performance Debt: Known slow paths, unoptimized queries, memory issues

Present the findings to the user.

Ask: "May I write these findings to docs/tech-debt-register.md?"

If yes, update the register (append new entries, do not overwrite existing ones). Verdict: COMPLETE — scan findings written to register.

If no, stop here. Verdict: BLOCKED — user declined write.


Phase 2B: Add Mode

Prompt for: description, category, affected files, estimated fix effort, impact if left unfixed.

Present the new entry to the user.

Ask: "May I append this entry to docs/tech-debt-register.md?"

If yes, append the entry. Verdict: COMPLETE — entry added to register.

If no, stop here. Verdict: BLOCKED — user declined write.


Phase 2C: Prioritize Mode

Read the debt register at docs/tech-debt-register.md.

Score each item by: (impact_if_unfixed × frequency_of_encounter) / fix_effort

Re-sort the register by priority score and recommend which items to include in the next sprint.

Present the re-prioritized register to the user.

Ask: "May I write the re-prioritized register back to docs/tech-debt-register.md?"

If yes, write the updated file. Verdict: COMPLETE — register re-prioritized and saved.

If no, stop here. Verdict: BLOCKED — user declined write.


Phase 2D: Report Mode

Read the debt register. Generate summary statistics:

  • Total items by category
  • Total estimated fix effort
  • Items added vs resolved since last report
  • Trending direction (growing / stable / shrinking)

Flag any items that have been in the register for more than 3 sprints.

Output the report to the user. This mode is read-only — no files are written. Verdict: COMPLETE — debt report generated.


Phase 3: Next Steps

  • Run /sprint-plan to schedule high-priority debt items into the next sprint.
  • Run /tech-debt report at the start of each sprint to track debt trends over time.

Debt Register Format

## Technical Debt Register
Last updated: [Date]
Total items: [N] | Estimated total effort: [T-shirt sizes summed]

| ID | Category | Description | Files | Effort | Impact | Priority | Added | Sprint |
|----|----------|-------------|-------|--------|--------|----------|-------|--------|
| TD-001 | [Cat] | [Description] | [files] | [S/M/L/XL] | [Low/Med/High/Critical] | [Score] | [Date] | [Sprint to fix or "Backlog"] |

Rules

  • Tech debt is not inherently bad — it is a tool. The register tracks conscious decisions.
  • Every debt entry must explain WHY it was accepted (deadline, prototype, missing info)
  • "Scan" should run at least once per sprint to catch new debt
  • Items older than 3 sprints without action should either be fixed or consciously accepted with a documented reason
how to use tech-debt

How to use tech-debt on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tech-debt
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/Donchitos/Claude-Code-Game-Studios --skill tech-debt

The skills CLI fetches tech-debt from GitHub repository Donchitos/Claude-Code-Game-Studios and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tech-debt

Reload or restart Cursor to activate tech-debt. Access the skill through slash commands (e.g., /tech-debt) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Accelerate Code Development

Use skill to generate boilerplate code, refactor legacy code, and write tests faster

Example

Generate React component with TypeScript types, styled-components, and comprehensive test suite in minutes

Reduce development time by 40-60% for repetitive coding tasks

Code Review Automation

Systematically review code for bugs, security issues, and style violations

Example

Analyze pull requests for common anti-patterns, suggest performance improvements, flag security vulnerabilities

Catch 70%+ of code issues before human review, improve code quality

Debug Complex Issues

Trace errors through stack traces and identify root causes faster

Example

Analyze error logs, suggest probable causes, recommend fixes with code examples

Cut debugging time by 30-50%, especially for unfamiliar codebases

Learn New Technologies

Get explanations, examples, and best practices for unfamiliar frameworks

Example

Understand Next.js app router, learn Rust ownership, grasp Kubernetes concepts with practical examples

Accelerate learning curve by 2-3x, reduce onboarding time for new tech stacks

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill installation support
  • Basic understanding of programming concepts and version control (Git)
  • Code editor or IDE for testing generated code (VS Code, JetBrains, etc.)
  • Test environment separate from production for validating skill outputs

Time Estimate

15-30 minutes to install and see first useful output

Installation Steps

  1. 1.Install the skill using provided installation command
  2. 2.Verify skill is loaded in Claude Desktop (check ~/.claude/skills directory)
  3. 3.Test skill with simple prompt: 'Help me review this code snippet'
  4. 4.Gradually increase complexity: code generation → refactoring → architecture advice
  5. 5.Review all generated code before committing to repository
  6. 6.Iterate on prompts to improve output quality and relevance
  7. 7.Share effective prompts with team for consistency

Common Pitfalls

  • Blindly trusting generated code without testing—always run tests and manual review
  • Not providing enough context about your project structure and coding standards
  • Expecting perfection on first generation—iteration and refinement are normal
  • Sharing proprietary code or API keys in prompts—maintain confidentiality
  • Over-relying on skill for critical security or business logic code
  • Skipping documentation of why AI-generated code was chosen over alternatives

Best Practices

✓ Do

  • +Always review and test AI-generated code before merging
  • +Provide clear context: language, framework, coding standards, constraints
  • +Use for boilerplate, tests, docs—areas where mistakes are easily caught
  • +Iterate on prompts: start broad, refine with specific requirements
  • +Combine AI suggestions with human judgment and domain expertise
  • +Document successful prompt patterns for team reuse
  • +Keep version control so you can rollback if needed
  • +Use skill for learning and exploration, not production-critical features initially

✗ Don't

  • Don't commit AI code without thorough testing and review
  • Don't expose sensitive code, credentials, or proprietary algorithms
  • Don't use for security-critical code (auth, crypto, payments) without expert review
  • Don't skip peer review process just because AI generated it
  • Don't assume code follows your team's conventions—verify
  • Don't let junior developers skip learning fundamentals by relying solely on AI
  • Don't ignore compiler warnings or test failures in generated code

💡 Pro Tips

  • Describe desired patterns explicitly: 'Use async/await, avoid callbacks'
  • Ask for alternatives: 'Show 3 approaches to solve this, with tradeoffs'
  • Request explanations: 'Explain why this approach is better than X'
  • Use skill for 70% generation + 30% manual refinement for best results
  • Build a prompt library for common patterns (API endpoints, components, tests)
  • Pair program with AI: describe problem → review solution → iterate → refine

When to Use This

✓ Use When

Use coding skills for boilerplate generation, code reviews, refactoring legacy code, writing tests, learning new frameworks, and debugging non-critical issues. Best for repetitive tasks where errors are easy to catch.

✗ Avoid When

Avoid for production security features (auth, encryption, payment processing), complex business logic requiring deep domain knowledge, performance-critical algorithms, or when learning fundamentals is more valuable than speed.

Learning Path

  1. 1Start with simple tasks: generate functions, write tests, explain code
  2. 2Progress to code review: analyze PRs, suggest improvements
  3. 3Advanced: architectural decisions, refactoring strategies, performance optimization
  4. 4Expert: use for exploring new paradigms, researching best practices, mentoring juniors

Integration

  • VS Code
  • JetBrains IDEs
  • Cursor
  • GitHub Copilot
  • Git workflows

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.846 reviews
  • Mateo Thomas· Dec 16, 2024

    Registry listing for tech-debt matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aditi Gill· Dec 12, 2024

    Useful defaults in tech-debt — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • James Reddy· Dec 12, 2024

    I recommend tech-debt for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kabir Malhotra· Nov 7, 2024

    Solid pick for teams standardizing on skills: tech-debt is focused, and the summary matches what you get after install.

  • Aditi Rao· Nov 3, 2024

    I recommend tech-debt for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kabir Chawla· Oct 26, 2024

    We added tech-debt from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Amelia Taylor· Oct 22, 2024

    tech-debt reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sofia Chen· Sep 21, 2024

    tech-debt has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Oshnikdeep· Sep 9, 2024

    tech-debt reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Piyush G· Sep 5, 2024

    Keeps context tight: tech-debt is the kind of skill you can hand to a new teammate without a long onboarding doc.

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