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

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 agentusable skills. We present COLLEAGUE.SKILL (https://github.com/titanwings/collea gue-skill), 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.

Introduction. The role of LLM agents is shifting from executing isolated instructions toward carrying reusable context about how work and interaction should be performed. In practice, users often want an agent to preserve bounded parts of a person’s expertise, memory, or interpersonal style: a teammate’s review judgment, a specialist’s decision heuristics, a public thinker’s mental models, or private interpersonal interaction patterns. Rather than treating this demand as unrestricted person simulation, we frame it as person-grounded trace-toskill distillation: turning traces of a person or role into a constrained artifact that makes useful knowledge, interaction style, and limits of use explicit. This framing does not claim identity replacement, and it treats the generated object as an editable technical artifact rather than as the person. LLM agents increasingly rely on modular extensions. Tools connect agents to external actions, while skills package domain knowledge, procedures, scripts, and reference materials that can be discovered and loaded on demand.

Discussion / Conclusion. Why the workflow matters. A single prompt can mimic surface behavior, but it rarely makes the extracted person-grounded knowledge accountable. COLLEAGUE.SKILL treats trace-to-skill distillation as a workflow over files: creation, inspection, invocation, correction, rollback, deletion, host installation, and optional distribution. These operations are not auxiliary engineering details. They are the conditions under which a generated person-grounded skill can be audited, repaired, withheld, or shared. They also make the research object sharper. Extraction quality can be inspected at the level of work.md and persona.md; installation and sharing can operate through manifests rather than ad hoc instructions; and governance can operate on explicit metadata rather than hidden prompt state. Behavioral fidelity frontier.