Agent skill · Security

tech-debt-analyzer

This skill should be used when analyzing technical debt in a codebase, documenting code quality issues, creating technical debt registers, or assessing code maintainability. Use this for identifying code smells, architectural issues, dependency problems, missing documentation, security vulnerabilities, and creating comprehensive technical debt documentation.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill tech-debt-analyzer-travisjneuman-claude-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: none
Path: skills/analysis/tech-debt-analyzer-travisjneuman-claude-2/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Technical Debt Analyzer Systematically identify, analyze, and document technical debt. ## When to Use **Use for:** - Analyzing code quality issues - Creating technical debt registers - Assessing code maintainability - Identifying dependency problems - Documenting security vulnerabilities - Planning refactoring efforts **Don't use when:** - Writing new code → use `generic-feature-developer` - Code review → use `generic-code-reviewer` - Writing tests → use `test-specialist` ## Quick Analysis Commands ```bash # Find large files (>500 lines) find src -name "*.ts" -exec wc -l {} + | awk '$1 > 500' | sort -rn # Find TODO/FIXME markers grep -rn "TODO\|FIXME\|HACK\|XXX" src/ # Check for console.log in production code grep -rn "console.log" src/ --include="*.ts" --include="*.tsx" # Find TypeScript 'any' usage grep -rn ": any" src/ --include="*.ts" --include="*.tsx" # Check outdated dependencies npm outdated # Security vulnerabilities npm audit # Unused exports (requires ts-unused-exports) npx ts-unused-exports tsconfig.json ``` ## Debt Categories | Category | Examples | | ------------- | ------------------------------------------------------ | | Code Quality | Large files, complex functio

What's inside
Steps it walks through
  1. When to Use
  2. Quick Analysis Commands
  3. Debt Categories
  4. Analysis Workflow
  5. 1. Automated Detection
  6. 2. Severity Assessment
  7. 3. Priority Matrix
  8. Debt Register Format
  9. Prevention Strategies
  10. Automated Guards
  11. Maintenance Schedule
  12. Self-Critique Checklist
  13. See Also
Ships with 1 file
  • metadata.json
Commands it runs
Find large files (>500 lines)
find src -name "*.ts" -exec wc -l {} + | awk '$1 > 500' | sort -rn
Find TODO/FIXME markers
grep -rn "TODO\|FIXME\|HACK\|XXX" src/
Check for console.log in production code
grep -rn "console.log" src/ --include="*.ts" --include="*.tsx"
Find TypeScript 'any' usage
grep -rn ": any" src/ --include="*.ts" --include="*.tsx"
Check outdated dependencies
npm outdated
More from claude-skill-registry
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About this skill
What does the tech-debt-analyzer skill do?

This skill should be used when analyzing technical debt in a codebase, documenting code quality issues, creating technical debt registers, or assessing code maintainability. Use this for identifying code smells, architectural issues, dependency problems, missing documentation, security vulnerabilities, and creating comprehensive technical debt documentation.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill tech-debt-analyzer-travisjneuman-claude-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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