skillpack-harvest

Name: skillpack-harvest
Availability: InStock
Author: starlink-awaken

Migrate fork-specific automation skills into a centralized repository bundle.

Updated Jun 10, 2026

Search Tags:#automation #deployment #refactoring #harvest #skillpack #gbrain

Ready to run

npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill skillpack-harvest-starlink-awaken

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skillpack-harvest
Source: https://github.com/starlink-awaken/omostation-gbrain/tree/main/skills/skillpack-harvest
Command: npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill skillpack-harvest-starlink-awaken

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of fragmented skill development by providing a structured editorial workflow to lift mature, fork-specific skills into the central gbrain bundle for universal access.

Core Features & Use Cases

Editorial Genericization: Provides a systematic checklist to scrub fork-specific names, entities, and internal conventions from your code.
Privacy-First Linting: Automatically scans for sensitive patterns and proper nouns to ensure harvested skills are safe for public distribution.
Use Case: You have developed a custom automation in your private OpenClaw fork that has proven highly effective; use this skill to generalize it and contribute it back to the main gbrain repository so other clients can benefit.

Quick Start

Invoke the harvest skill by providing the target slug and the path to your host repository to begin the editorial and linting process.

Frequently Asked Questions about skillpack-harvest

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

FAQPage Schema

How do I migrate automation skills from a private fork to a central repository?▼

To migrate automation skills from a private fork to a central repository, use a structured editorial workflow that manages code genericization and enforces privacy-linting standards before distribution. This ensures consistent, non-destructive skill harvesting across multiple client environments.

What is editorial genericization for fork-specific code?▼

Editorial genericization for fork-specific code is a systematic checklist process to scrub internal names, entities, and conventions from your automation scripts. This prepares mature, fork-specific skills for universal access in a centralized repository bundle.

How do I scan automation code for sensitive patterns before public distribution?▼

You scan automation code for sensitive patterns before public distribution using privacy-first linting standards. This process automatically detects sensitive patterns and proper nouns to ensure harvested skills are safe for universal access across multiple client environments.

Can I contribute custom automation from an OpenClaw fork back to a main repository?▼

Yes, you can contribute custom automation from an OpenClaw fork back to a main repository by using a standardized, non-destructive copy workflow. This workflow lifts proven automation skills into the central gbrain bundle for universal client access.

What is the best way to distribute mature skills across multiple client environments?▼

The best way to distribute mature skills across multiple client environments is through a standardized, non-destructive copy workflow within a centralized repository bundle. This ensures consistent skill distribution while enforcing privacy-linting and editorial genericization standards.

When do I need to use a privacy-linting workflow for skill harvesting?▼

You need to use a privacy-linting workflow for skill harvesting when preparing mature, fork-specific automation skills for a centralized repository. It ensures all code, documentation, and configuration files are scrubbed of sensitive patterns before public distribution.