SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History

Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change. Existing methods improve skills in bounded runs and retain only the final artifact, discarding the decision history that later agents need to interpret prior revisions, evaluations, and rejected alternatives. We introduce SkillHone, a harness for continual agent skill evolution grounded in persistent decision history. SkillHone pairs skill revisions with evaluation-side evidence that supplies practice feedback, recording structured histories of diagnoses, revisions, evidence, and outcomes. Role-separated subagents run candidate skills on practice probes with redacted reporting and propose revisions informed by prior decisions, enabling cross-session refinement without rediscovering past rationale. We evaluate SkillHone on deep-research benchmarks in a raw open-web setting, where agents are not given an integrated search stack and must organize retrieval through portable skills. We compare against a deep-research agent backed by commercial retrieval services. With Qwen3.6-35B-A3B as the evaluation-time backbone, the resulting skills outperform the deep-research agent by 15.8 points on GAIA and 3.2 points on WebWalkerQA-EN, while also exceeding prior skill-evolution methods.