std-clean-sweep

Review code changes for correctness, remove dead code, and validate formatting.

Updated Jan 26, 2026
One-click install
npx skills add https://github.com/thehivegroup-ai/ai-development --skill std-clean-sweep
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: std-clean-sweep
Source: https://github.com/thehivegroup-ai/ai-development/tree/main/.cursor/skills/std-clean-sweep
Command: npx skills add https://github.com/thehivegroup-ai/ai-development --skill std-clean-sweep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps in refactoring and standardizing code changes to ensure a clean, consistent output, which is crucial for maintaining code quality and facilitating smooth handoffs or reviews.

Core Features & Use Cases

  • Code Refactoring: Removes dead code, TODOs, and debug artifacts.
  • Formatting Validation: Ensures consistent formatting, naming, and structure.
  • Change Summarization: Provides a summary of what changed and any known gaps.
  • Use Case: After implementing changes in a codebase, use this Skill to clean up the code before a handoff or review to ensure consistency and quality.

Quick Start

Run the std-clean-sweep skill to automatically clean up and standardize your code changes.

Frequently Asked Questions about std-clean-sweep

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate code cleanup and standardization before a handoff?

Automating code cleanup before a handoff requires reviewing changes for correctness, removing dead code, and validating formatting. This Skill standardizes naming and structure while summarizing changes and known gaps to ensure consistent output quality.

What is the best way to remove dead code and debug artifacts after implementing changes?

Removing dead code and debug artifacts after implementing changes is handled by refactoring and standardization processes. It validates formatting, ensures structural consistency, and provides a summary of what changed to verify codebase quality.

How does code standardization improve code quality for post-change reviews?

Code standardization improves post-change review quality by ensuring consistent formatting and structure while validating correctness. It removes TODOs and dead code, facilitating smooth reviews and handoffs by maintaining a clean, consistent codebase.

When do I need to run code refactoring for formatting validation?

Code refactoring for formatting validation is needed after implementing changes in a codebase, specifically before a handoff or review. It ensures consistency by reviewing changes, removing dead code, and validating naming conventions and structure.

Can I use code cleanup scripts to summarize codebase changes and known gaps?

Code cleanup scripts can summarize codebase changes and known gaps as part of the standardization process. They review changes for correctness, remove debug artifacts, and validate formatting to provide a clear summary of modifications.

What are the limitations of automating code standardization for post-change reviews?

Automating code standardization for post-change reviews focuses on formatting, dead code removal, and correctness validation. It is designed specifically for post-change contexts to ensure quality and consistency, rather than serving as an initial development tool.