SIGIL: Compiling Agent Skills into Typed Harnesses

Abstract

Agent skills provide a reusable way to specify multi-step agent behavior, but they remain natural-language specifications interpreted by the model at runtime. As a result, required tool calls, ordering constraints, and checks may be skipped even when explicitly prescribed by the skill. We introduce Skill Compilation, a paradigm that translates natural-language skills into executable agent programs while preserving model judgment where semantic decisions are required. We realize this idea in SIGIL. SIGIL extracts source-grounded requirements, decomposes them using a closed Agent Instruction Set (AIS), composes them into AG-IR with explicit ownership, data flow, and control flow, and deterministically lowers validated AG-IR into executable code. Across 33 publicly available SKILL.md files and three runtime models, SIGIL increases mean Applicable-Mandate Compliance (AMC), the fraction of applicable skill requirements satisfied during execution, from 66.0% with direct skill execution to 88.6%. SIGIL also reduces total runtime token consumption by 2.40-5.95x. These results show that compiling procedural structure improves the reliability and efficiency of skill execution while retaining model judgment where it is needed.