pack-source-process

Gather and filter sources for AI agent pack building.

25|7|Updated Mar 16, 2026
One-click install
npx skills add https://github.com/xoai/sage --skill pack-source-process
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: pack-source-process
Source: https://github.com/xoai/sage/tree/main/skills/pack-source-process
Command: npx skills add https://github.com/xoai/sage --skill pack-source-process

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that AI agents use the most relevant and accurate information for building packs, by filtering out general documentation and focusing on sources that highlight common agent mistakes and their corrections.

Core Features & Use Cases

  • Prioritizes failure-focused sources: Emphasizes migration guides, GitHub issues, and changelogs over official documentation.
  • Extracts actionable insights: Identifies specific agent mistakes and provides corrections.
  • Deduplicates and ranks information: Ensures the most critical insights are presented.
  • Handles project-specific overlays: Adapts community pack guidance to project-specific needs.
  • Use Case: When building a new pack for a software library, this Skill will analyze community discussions and bug reports to identify pitfalls developers commonly encounter, ensuring the pack guides the agent away from these errors.

Quick Start

Process the provided pack brief to gather and refine relevant sources.

Frequently Asked Questions about pack-source-process

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

FAQPage Schema
How do I filter software documentation to prevent AI agent mistakes?

To refine sources for agent accuracy, you process a pack brief to gather and filter information, prioritizing migration guides, GitHub issues, and changelogs to extract actionable insights and correct common agent mistakes.

What is the best way to extract anti-patterns from GitHub issues for pack building?

The best way to extract anti-patterns from GitHub issues for pack building is to analyze them alongside migration guides and changelogs, deduplicating and ranking the extracted insights to guide agents away from common errors.

How do I handle project-specific overlays when building an AI agent pack?

Handling project-specific overlays involves identifying deviations from community pack guidance, allowing you to adapt general source information to the specific needs and constraints of your software project.

Why prioritize migration guides and changelogs over official documentation for agent accuracy?

Prioritizing migration guides and changelogs over official documentation for agent accuracy ensures the focus remains on failure-focused sources that highlight common agent mistakes and their corrections, rather than untested general information.

Can I use source processing to deduplicate insights for a software library pack?

Yes, you can use source processing to deduplicate insights for a software library pack by gathering community discussions and bug reports, then ranking the information so only the most critical anti-patterns are included.

What are the limitations of using general documentation for AI agent pack building?

The limitation of using general documentation for AI agent pack building is that it often lacks failure-focused insights, missing the specific agent mistakes and corrections found in GitHub issues and changelogs.