Where Natural Language Ends in Agents: Executable Skill Composition with MetaSkill

Key points

• We propose selective formalization at Skill boundaries: coordination relations with stable references and machine-checkable constraints become executable, while substantive task judgments remain within Skills.1
• We introduce MetaSkill, a hybrid representation that preserves complete Skill packages and local instructions while making invocation dependencies and predecessor-text bindings executable through a directed acyclic graph (DAG) runtime.1
• Across 20 multi-Skill artifact tasks and two LLMs, we compare natural-language procedures, prompt-visible graphs, and MetaSkill derived from a common frozen plan per task, using the same designated Skill packages.1
• Together, these findings support MetaSkill as a practical interface for composing open-ended Skills, combining local linguistic flexibility with executable dependencies, explicit predecessor-text bindings, and auditable coordination.1

Sources (1)

• [1]Where Natural Language Ends in Agents: Executable Skill Composition with MetaSkill
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 02:34 PM

  Agent Skills express open-ended task knowledge in natural language, but composing them requires persistent execution order and upstream-output handoffs.

  We propose selective formalization at Skill boundaries: coordination relations with stable references and machine-checkable constraints become executable, while substantive task judgments remain within Skills.

Extractive summary: sentences quoted from the sources.