SIGIL: Skill Compilation for Reliable and Efficient Agent Execution

Agent skills describe reusable procedures, but runtime models must still interpret their instructions and coordinate execution. We introduce Skill Compilation, which translates the procedure prescribed by an authored skill into an executable harness while preserving decisions left to the model. Our compiler, SIGIL, translates skills and their resources into a typed intermediate representation, validates it, and deterministically generates the harness. We evaluate SIGIL on 11 compliance-critical skills, where following the prescribed procedure is part of correctness. SIGIL improves adherence across all four runtime models, achieving up to 100% measured mean Skill Adherence compared with 26.2-54.0% for direct SKILL.md execution. On this suite, SIGIL reduces total runtime tokens by 21-45% for three of the four models. On SkillsBench, SIGIL also improves task performance across all four models, with absolute gains of 3.5-35.7 percentage points. These findings suggest that compiling reusable skill procedures into executable harnesses can improve adherence, task completion, and runtime efficiency.