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slop-cleaner

Detect codebase hygiene issues and generate prioritized cleanup reports.

2|Updated Jun 25, 2026
Search Tags:#technical debt#rust#dead code#architecture review#code hygiene#large files#slop scan
One-click install
npx skills add https://github.com/cc-claws/cc-code --skill slop-cleaner-cc-claws
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slop-cleaner
Source: https://github.com/cc-claws/cc-code/tree/main/.claude/skills/slop-cleaner
Command: npx skills add https://github.com/cc-claws/cc-code --skill slop-cleaner-cc-claws

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

This Skill helps you quickly assess codebase hygiene by finding structural issues such as oversized files, dead code, mixed test and implementation logic, and architectural smells.

Core Features & Use Cases

  • Large File Detection: Flags files that are likely too big or too complex and suggests where splitting would improve maintainability.
  • Dead Code and Tech Debt Scanning: Surfaces unused code, TODO-style leftovers, and suspiciously stale implementation details.
  • Test Separation Checks: Identifies inline tests that should be moved into dedicated test files to keep source modules focused.
  • Architecture Smell Review: Highlights overly broad utility modules, excessive public surface area, and other structural risks.
  • Use Case: Ask it to review a Rust repository before a release and it will produce a prioritized cleanup report with concrete file-level findings.

Quick Start

Ask the skill to scan the repository for large files, dead code, mixed tests, and architecture smells, then return a prioritized cleanup report.

Frequently Asked Questions about slop-cleaner

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

FAQPage Schema
How do I scan a Rust codebase for dead code and technical debt?▼

To scan a Rust codebase for dead code and technical debt, you can detect unused code, TODO-style leftovers, stale implementation details, and architectural smells, generating a prioritized cleanup report.

What is the best way to detect oversized files and architecture smells in a repository?▼

Detecting oversized files and architecture smells requires analyzing file inventory and line counts to flag overly large files and broad utility modules, suggesting where splitting improves maintainability.

How do I identify inline tests that should be separated from source modules?▼

Identifying inline tests that should be separated from source modules requires running test separation checks to find mixed test and implementation logic, keeping source modules focused.

Can I generate a prioritized code hygiene report before a software release?▼

Generating a prioritized code hygiene report before a release is possible by combining large file detection, dead code scanning, and architecture smell reviews into concrete file-level findings.

Does this structural quality analysis require any external dependencies?▼

Structural quality analysis requires no external dependencies to run, utilizing internal assets to perform fast file inventory, line-count analysis, and dead-code detection directly on the repository.

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