A public prompt cut Agent Harness token cost by 7% with no quality loss

团队分享提升 Agent Harness Token 效率的提示词

A team shared a public prompt to optimize LLM Agent Harness, cutting per-task token cost by ~7% without quality loss through prompt trimming, tool offloading, cache layout, sparse line numbers, and sub-agent tuning. The prompt advises metering by task, not by request, and mapping the harness, measuring baseline, then prioritizing changes. The post doesn't disclose the model used, task set, or baseline token cost.