package

Remove build artifacts, caches, and secrets from Python project directories.

Updated Apr 28, 2026
One-click install
npx skills add https://github.com/yao9906/visrag --skill package-yao9906
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: package
Source: https://github.com/yao9906/visrag/tree/main/.claude/skills/package
Command: npx skills add https://github.com/yao9906/visrag --skill package-yao9906

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill removes clutter, build outputs, caches, virtual environments, logs, and exposed secrets so a project can be safely shared or distributed.

Core Features & Use Cases

  • Project Cleanup: Deletes Python caches, build artifacts, IDE files, coverage outputs, and other generated clutter.
  • Secret Sanitization: Replaces sensitive values in config files with placeholders before handoff.
  • Distribution Prep: Creates a minimal repository state for packaging, archival, or sending a clean copy to another team.

Quick Start

Use the package skill to clean the project for distribution and keep data and logs only if you want them preserved.

Frequently Asked Questions about package

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

FAQPage Schema
How do I remove Python caches and build artifacts before distributing a project?

To remove Python caches and build artifacts, run a project cleanup process that deletes generated clutter like __pycache__ directories and virtual environments. This sanitizes the codebase for safe distribution and repository handoff.

What is the best way to sanitize secrets in config files for repository handoff?

Secret sanitization replaces sensitive values in configuration files like settings.yaml with placeholders before handoff. This ensures clean-copy delivery workflows do not expose embedded credentials during project distribution.

Can I preserve data and logs while cleaning a Python project for distribution?

You can preserve data and logs while cleaning a Python project for distribution. The process supports optional preservation of these files during sanitization, requiring dry-run review and confirmation before deleting other build artifacts.

Does project cleanup require a dry-run review before deleting caches?

Project cleanup requires a dry-run review and confirmation before deletion. This precaution ensures you can verify which build artifacts, virtual environments, and IDE files will be removed from the project directory.

When do I need to sanitize a codebase for clean-copy delivery?

You need to sanitize a codebase for clean-copy delivery when preparing a minimal repository state for packaging, archival, or sending to another team. This removes clutter and exposed secrets for safe distribution.