💡 The paper introduces COLLEAGUE SKILL, a system for automatically generating person-grounded AI skills from heterogeneous traces of expert knowledge. The problem addressed is that current methods for creating AI agents that mimic human expertise and judgment are limited, as they rely on fragmented evidence and lack a comprehensive workflow for distilling this knowledge into usable skills.

The method presented involves an automated trace-to-skill distillation process that takes materials from a target person or role and produces a versioned skill package with two tracks: a capability track for practices, mental models, and decision heuristics, and a bounded behavior track for communication style, interaction rules, and correction history. This package can be inspected, updated through natural-language feedback, and deployed across agent hosts.

The results of the system are significant, with the open-source repository having approximately 18.5k GitHub stars, 215 skills from 165 contributors, and over 100k cumulative stars across listed skill cards. The system demonstrates how person-grounded skills can be represented as portable, correctable packages, rather than opaque prompts or hidden memories, and provides a comprehensive workflow for generating and deploying these skills. The paper presents the artifact contract, generation workflow, correction lifecycle, deployment surface, and domain presets implemented in the system, showcasing its potential for creating more human-like AI agents.