package

Removes build caches and sensitive config data from Python project repositories.

Updated Mar 5, 2026
One-click install
npx skills add https://github.com/ljk486/Rag-agent --skill package-ljk486
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: package
Source: https://github.com/ljk486/Rag-agent/tree/main/MODULAR-RAG-MCP-SERVER-clean-start/.github/skills/package
Command: npx skills add https://github.com/ljk486/Rag-agent --skill package-ljk486

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill removes local development clutter so you can produce a clean, distributable repository without manual file hunting, accidental secret leaks, or leftover build noise.

Core Features & Use Cases

  • Dry-run cleanup: Preview exactly what will be removed before any deletion happens.
  • Safe packaging workflow: Execute a confirm-then-clean flow that supports keeping data and logs when needed.
  • Secret sanitization: Replace sensitive values in config files so the project can be shared more safely.
  • Use Case: Prepare a Python project for handoff, release, or code review by stripping caches, virtual environments, logs, and generated artifacts.

Quick Start

Use the package skill to clean this repository for distribution and keep data and logs if you want to preserve existing ingested content.

Frequently Asked Questions about package

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

FAQPage Schema
How do I clean build artifacts and caches from a Python repository before sharing?

To clean build artifacts and caches from a Python repository, use a sanitization workflow that identifies and removes generated files. The package Skill strips clutter like virtual environments and logs to produce a distributable codebase.

Can I preview what files will be removed before sanitizing my repository?

Yes, you can preview repository sanitization changes using a dry-run cleanup report. This feature shows exactly what will be removed before any deletion happens, ensuring safe packaging without accidental data loss.

How do I remove secrets and sensitive values from config files for distribution?

Secret sanitization replaces sensitive values in config files so the project can be shared more safely. This process targets configuration files to strip sensitive data before repository handoff or release.

How do I prepare a Python project for handoff while keeping data and logs?

Preparing a Python project for handoff while keeping data is supported through an optional data retention flow. The confirm-then-clean execution supports preserving existing ingested content and logs during workspace sanitization.

What's the best way to sanitize a workspace before code review?

Workspace sanitization before code review is best handled by a confirm-before-execute flow that removes development clutter. This approach strips local build noise and leftover artifacts without manual file hunting.