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techdebt
// Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up.
// Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up.
| name | techdebt |
| description | Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up. |
| workflow_stage | engineering |
| compatibility | ["claude-code","cursor","codex","gemini-cli"] |
| author | Awesome Econ AI Community |
| version | 1.0.0 |
| disable-model-invocation | true |
| tags | ["refactoring","code-quality","maintenance"] |
Identify and fix technical debt in the codebase.
typing.List instead of listScan the Codebase
Report Findings
Fix Issues
Verify
ruff check .pytestOptionally specify a directory or file to focus on.
Usage:
/techdebt - Scan entire project/techdebt src/ - Scan specific directory/techdebt src/utils.py - Scan specific fileProvide a summary of:
Simplify and clean up code after changes are complete. Reduces complexity, improves readability, and ensures consistency.
Commit changes, push to remote, and create a pull request. Use for completing features or fixes ready for review.
Implements the Spec-Driven Development lifecycle (Intent, Requirements, Design, Tasks, Build) for structured feature development. Use when the user wants to scaffold a new feature spec, generate EARS requirements, create a technical design, break work into tasks, or check spec status. Trigger on keywords: sdd, spec-driven, ears requirements, feature spec.
Run IV, DiD, and RDD analyses in R with proper diagnostics
Build and solve Walrasian general equilibrium models with theory derivations and Julia computation
Panel data analysis with Python using linearmodels and pandas.