tech-debt▌
Donchitos/Claude-Code-Game-Studios · updated May 19, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
### 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
| 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 indicatorsadd— Add a new tech debt entry manuallyprioritize— Re-prioritize the existing debt registerreport— 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:
TODOcomments (count and categorize)FIXMEcomments (these are bugs disguised as debt)HACKcomments (workarounds that need proper solutions)@deprecatedmarkers- 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- Documentation Debt: Missing docs, outdated docs, undocumented APIs
- Dependency Debt: Outdated packages, deprecated APIs, version conflicts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-planto schedule high-priority debt items into the next sprint. - Run
/tech-debt reportat 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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tech-debt from GitHub repository Donchitos/Claude-Code-Game-Studios and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install the skill using provided installation command
- 2.Verify skill is loaded in Claude Desktop (check ~/.claude/skills directory)
- 3.Test skill with simple prompt: 'Help me review this code snippet'
- 4.Gradually increase complexity: code generation → refactoring → architecture advice
- 5.Review all generated code before committing to repository
- 6.Iterate on prompts to improve output quality and relevance
- 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▌
- 1Start with simple tasks: generate functions, write tests, explain code
- 2Progress to code review: analyze PRs, suggest improvements
- 3Advanced: architectural decisions, refactoring strategies, performance optimization
- 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.
Ratings
4.8★★★★★46 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.
showing 1-10 of 46