COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation

2026-05-29

AI Summary

This work addresses the challenge of automatically distilling structured, verifiable, and revisable skills from heterogeneous expert behavioral traces to construct personalized AI agents that embody human expertise, judgment, and interaction styles. The authors propose an end-to-end expert knowledge distillation framework that transforms multimodal source materials into versioned skill packages comprising capability trajectories—encompassing practices, mental models, and decision heuristics—and boundary trajectories capturing communication styles, interaction norms, and revision histories. This approach is the first to enable fully automated generation of structured AI skill packages that are portable, auditable, rollback-capable, and deployable across diverse agents, thereby overcoming key limitations of conventional prompt engineering and memory systems. An open-source implementation has garnered 18.5k GitHub stars, with 215 community-contributed skills from 165 contributors, demonstrating the feasibility and adoption of human-centric skill packaging.

Abstract

LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and interaction style. Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona systems capture fragments of this evidence, while skill frameworks provide portable packaging formats; however, there is no end-to-end workflow for distilling these traces into inspectable, correctable, and agent-usable skills. We present an automated trace-to-skill distillation system for generating person-grounded AI skills via expert knowledge distillation. Given materials from a target person or role, COLLEAGUE.SKILL produces a versioned skill package with two coordinated tracks: a capability track for practices, mental models, and decision heuristics, and a bounded behavior track for communication style, interaction rules, and correction history. The package can be inspected, invoked, updated through natural-language feedback, rolled back, installed across agent hosts, and optionally prepared for controlled distribution. We describe the artifact contract, generation workflow, correction lifecycle, deployment surface, and domain presets implemented in the open-source system. At the time of writing, the public repository has approximately 18.5k GitHub stars; the gallery lists 215 skills from 165 contributors and more than 100k cumulative stars across listed skill cards. The system illustrates how person-grounded skills can be represented as portable, correctable packages rather than opaque prompts or hidden memories.

Problem

• expert knowledge distillation
• person-grounded AI
• skill generation
• heterogeneous traces
• inspectable skills

Innovation

• knowledge distillation
• person-grounded AI
• skill packaging
• trace-to-skill
• correctable agents