ConCord: Distilling and Coordinating Persona Skills from Multi-User Work Records

Persona skills distill working practices and preferences from human work records into reusable instructions for Large Language Model (LLM) agents. Recent methods combine records from multiple users to provide broader knowledge, but different users may recommend incompatible actions for the same situation. Existing methods do not explicitly preserve and coordinate such conflicts, which can leave agents uncertain about which practice to follow. We present ConCord, an automated framework for constructing and coordinating persona skills from multi-user work records. ConCord first extracts context-dependent decision rules from each user's records, and then groups users with similar practices and constructs one skill for each group, sharing knowledge across users while preserving conflicting approaches in separate skills. For each task, separate LLM agents independently generate candidate responses using different skills. A merging LLM then combines the responses and resolves conflicting recommendations based on the task requirements. We evaluated ConCord on four datasets of user work records under different scenarios that cover workplace communication, software issue handling, and computer-science question answering. ConCord achieves an average score improvement of $0.34$ over responses generated without skills, compared with $0.14$ for the strongest baseline. These results show that preserving conflicting practices during skill construction and resolving conflicts for each task improves multi-user persona skill coordination. We release the replication package at the anonymous link: https://anonymous.4open.science/r/ConCord-F060.