Techdebt

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/engineering/techdebt

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Install

npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill techdebt

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One thing to look at

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What its author says it does

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Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up.

SKILL.md

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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Outdated Patterns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

Scan the Codebase
- Look for patterns matching the issues above
- Prioritize by impact and ease of fix
Report Findings
- List issues by category
- Include file paths and line numbers
- Estimate severity (high/medium/low)
Fix Issues
- Start with high-severity, easy fixes
- Create atomic commits for each fix
- Run tests after each change
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