Summary

This repository, GBSOSS/skill-from-masters, offers an AI skill designed to elevate the creation of new AI skills by embedding proven methodologies from domain experts. It addresses the challenge of incorporating established best practices and frameworks before code generation, ensuring AI skills are built on a foundation of world-class expertise. This benefits AI developers and users by providing a mechanism to infuse deep domain knowledge into AI agents from their inception.

How It Works

The core approach involves a multi-stage discovery process. It begins by querying a local methodology database, then expands to web searches for additional experts and primary sources. The skill identifies "golden examples" of exemplary outputs and searches for common "anti-patterns" to avoid. A key feature is cross-validation across multiple expert sources to establish consensus and highlight disagreements, ultimately extracting actionable principles. These principles are then presented to the user for selection before the final skill is generated by an underlying skill-creator tool.

Quick Start & Requirements

• Primary Install: Clone directly into your skills directory (e.g., ~/.claude/skills/).
• Prerequisites: Requires skill-creator to be available in your environment.
• Links: Installation URL: https://github.com/anthropics/skill-from-masters.git

Highlighted Details

• 3-Layer Search: Employs a local database, web search for experts, and deep dives into primary sources.
• Methodology Database: Features a curated database covering over 15 domains (e.g., Writing, Product, Sales, Hiring, User Research, Engineering, Leadership, Negotiation, Startups, Decision Making) with example experts like Barbara Minto, Marty Cagan, Neil Rackham, Laszlo Bock, Rob Fitzpatrick, Martin Fowler, Kim Scott, Chris Voss, Eric Ries, Jeff Bezos, and Charlie Munger. Includes an "Oral Tradition" section for figures like Steve Jobs and Elon Musk.
• Golden Examples & Anti-Patterns: Identifies exemplary outputs to set a quality bar and searches for common mistakes to avoid.
• Cross-Validation: Compares insights from multiple experts to find consensus and flag disagreements.
• Quality Checklist: A pre-generation verification step ensuring comprehensive research, source quality, example identification, anti-pattern analysis, cross-validation, and actionable principle encoding.

Maintenance & Community

The provided README does not detail specific contributors, sponsorships, partnerships, roadmaps, or community channels (e.g., Discord, Slack). Contributions are welcomed via pull requests.

Licensing & Compatibility

• License: MIT License.
• Compatibility: The MIT license is permissive, allowing for use, modification, and distribution, suggesting broad compatibility for commercial and closed-source applications.

Limitations & Caveats

The skill's efficacy is contingent on the comprehensiveness of its internal database and the success of its web search capabilities. It operates as a component within a larger AI agent framework, requiring the presence of the skill-creator tool. The README does not specify any known bugs, alpha status, or unsupported platforms.