{
  "id": 19509,
  "url": "https://arxiv.org/abs/2608.10538",
  "title": "SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models",
  "summary": "Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution. However, because strong closed-source models entail high inference costs, current popular agent harnesses, such as Codex and OpenClaw, remain prohibitively expensive when deploying these skills to accomplish real-world tasks. The r",
  "authors": "Chenhao Dang, Siyuan Xiong, Conghui He, Weijia Li",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T20:00:00.000Z",
  "fetched_at": "2026-08-15T05:10:17.122Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/19509",
  "original_url": "https://arxiv.org/abs/2608.10538",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}