afc:clean

Automate pipeline artifact cleanup, dead code scanning, and AI memory management.

7|Updated Feb 19, 2026
One-click install
npx skills add https://github.com/jhlee0409/all-for-claudecode --skill afc-clean
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: afc:clean
Source: https://github.com/jhlee0409/all-for-claudecode/tree/main/skills/clean
Command: npx skills add https://github.com/jhlee0409/all-for-claudecode --skill afc-clean

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the cleanup of artifacts generated during a development pipeline, ensuring a clean codebase and efficient memory management.

Core Features & Use Cases

  • Artifact Cleanup: Removes temporary files and directories created by the pipeline.
  • Dead Code Scan: Identifies and helps remove unused code.
  • Memory Management: Persists relevant learnings and prunes old memory entries.
  • Use Case: After a feature has been fully implemented and reviewed, use this Skill to tidy up the project, remove any leftover pipeline-specific files, and ensure the AI's memory is up-to-date and efficient.

Quick Start

Run the clean phase of the pipeline for the current feature.

Frequently Asked Questions about afc:clean

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

FAQPage Schema
How do I automate pipeline artifact cleanup after feature implementation?

Automate pipeline artifact cleanup by running bash scripts that remove temporary pipeline files and directories. This ensures a clean codebase and efficient memory management after feature implementation and review.

What is dead code scanning and how does it improve code hygiene?

Dead code scanning identifies unused code segments within a project to help remove them. This improves code hygiene by ensuring the codebase remains clean and maintainable during pipeline cleanup.

How do I prune old memory entries in an automated development workflow?

Prune old memory entries by running the cleanup phase of the pipeline, which persists relevant learnings while removing outdated entries. This maintains an efficient knowledge base for AI memory.

Do I need bash scripts to manage pipeline state and delete artifacts?

Yes, you need bash scripts to manage pipeline state and delete artifacts. The cleanup process requires executing these scripts to remove temporary files and manage memory effectively.

When should I run a codebase cleanup in an automated development pipeline?

Run a codebase cleanup during the post-implementation phase after a feature is fully implemented and reviewed. This ensures temporary pipeline files are removed and the AI memory is updated.