Reusable skills guide large language model (LLM) agents through recurring requests, but each invocation still requires the model to interpret the skill and reconstruct its orchestration. Can agents automatically compile this orchestration into an executable fast path without human intervention? We first study zero-shot skill specialization, which compiles without execution evidence, and find that despite the skill's procedural guidance, incorrect assumptions about runtime bindings can make generated plans brittle and recovery costly. This motivates SkillCache, a one-trace skill specialization method that compiles a completed invocation into a parameterized capsule of programs and natural-language steps, replays it before reuse, and drives it with a plan executor that returns control to the model only when needed, keeping the source skill for recovery. With the Claude Code harness, warm execution of the same request reduces tokens on 87 SkillsBench tasks by 81% with Claude Sonnet 4.6 and by 74% with Claude Opus 4.8 at comparable reward, and capsules stay effective under controlled request changes. Capsules also transfer across models, raising three open-weight models' mean reward by 12-25 percentage points while reducing their tokens by 72-81%, suggesting that execution-grounded compilation is a promising approach to efficient repeated skill use and cross-model transfer.

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