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techdebt

techdebt

github.com/meleantonioDevelopment65753 installsUpdated 9w ago
Repository

About techdebt

A development skill for finding and addressing technical debt in a codebase. It scans for duplication, dead code, outdated patterns, and code smells, then reports, prioritizes, fixes, and verifies issues.

Key features

  1. 01Find duplication and dead code
  2. 02Flag outdated patterns and code smells
  3. 03Report paths, line numbers, and severity
  4. 04Prioritize and fix selected issues
  5. 05Run Ruff and pytest for verification

Use cases

  1. 01Audit a whole codebase for maintainability issues
  2. 02Focus a debt review on a file or directory
  3. 03Verify fixes with linting and tests

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/meleantonio/awesome-econ-ai-stuff --skill techdebt

After installing, type/techdebtin Claude Code to confirm it loaded.

Bundled filesindex.md

Details

From the project README

Technical Debt Finder

Identify and fix technical debt in the codebase.

What to Look For

Code Duplication
  • Functions with similar logic that could be consolidated
  • Copy-pasted code blocks
  • Repeated patterns that should be abstracted
Dead Code
  • Unused imports
  • Unused functions or classes
  • Commented-out code blocks
  • Unreachable code paths
Outdated Patterns
  • Deprecated API usage
  • Old-style string formatting (% or .format) vs f-strings
  • Type hints using typing.List instead of list
  • Missing type hints on public functions
Code Smells
  • Functions longer than 50 lines
  • Too many parameters (more than 5)
  • Deep nesting (more than 3 levels)
  • Magic numbers without constants
  • Overly complex conditionals
Missing Best Practices
  • Missing docstrings on public functions
  • Missing error handling
  • Hardcoded values that should be config
  • Missing tests for critical paths

Workflow

  1. Scan the Codebase

    • Look for patterns matching the issues above
    • Prioritize by impact and ease of fix

  2. Report Findings

    • List issues by category
    • Include file paths and line numbers
    • Estimate severity (high/medium/low)

  3. Fix Issues

    • Start with high-severity, easy fixes
    • Create atomic commits for each fix
    • Run tests after each change

  4. Verify

    • Run linter: ruff check .
    • Run tests: pytest
    • Ensure no new issues introduced

Arguments

Optionally specify a directory or file to focus on.

Usage:

  • /techdebt - Scan entire project
  • /techdebt src/ - Scan specific directory
  • /techdebt src/utils.py - Scan specific file

Output

Provide a summary of:

  • Issues found (by category)
  • Issues fixed
  • Remaining items for future sessions

Comments

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