Multi-Resolution Skill Compression Cuts LLM Prefill Cost for Repeated Workflows

This method compresses reusable procedural skills — natural language workflow descriptions injected into LLM contexts — at multiple resolution levels offline, reducing prefill token count and latency when the same skill is invoked repeatedly. Unlike document compression methods, it preserves logical dependencies between workflow steps and tool protocols.