Latent Briefing

If you're running orchestrator-worker agent systems and watching token costs compound as the orchestrator's trajectory gets replayed to every worker, this skill shows you how to share state at the KV cache level instead of text. It adapts Attention Matching compaction with task-guided queries and robust thresholding to keep only the positions the worker actually needs for its current subtask. The technique requires runtime access to KV tensors, so it won't work through standard APIs, but the documented results show material worker-token savings on long-document QA with low-single-digit-second overhead. It's a specialized primitive for systems where summarization is too lossy and full context replay is too expensive.