{
  "id": 4038,
  "url": "https://arxiv.org/abs/2605.19604v1",
  "title": "Formal Skill: Programmable Runtime Skills for Efficient and Accurate LLM Agents",
  "summary": "Large Language Model (LLM) agents increasingly act inside real workspaces, where tools and skills determine whether model reasoning becomes reliable action. Existing skills remain largely informal: Markdown skills and instruction packs encode procedures as long natural-language documents, while function calling, Model Context Protocol (MCP) servers, and framework tools structure individual actions but usually leave workflow state, policy enforcement, and completion discipline outside the skill i",
  "authors": "Xi Zhang, Meijun Gao, Yuntian Zhao, Xinyu Tan, Yilun Yao, Feiyu Wang et al.",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-19T09:43:17.000Z",
  "fetched_at": "2026-07-14T16:30:41.583Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4038",
  "original_url": "https://arxiv.org/abs/2605.19604v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}