What problem does it solve? LLM context windows are limited, and large tool outputs, long-running plans, and multi-agent communication quickly bloat or overflow the window, degrading agent performance. This Skill provides patterns for using the filesystem as an overflow layer so agents store, retrieve, and update context on demand instead of holding everything in the window. ## Core Features & Use Cases - Tool Output Offloading: Automatically write large tool outputs (over ~2000 tokens) to scratch files and return compact references with summaries. - Plan Persistence: Save structured multi-step plans to disk so agents can re-read objectives and progress after context refreshes or summarization. - Sub-Agent Workspaces & Dynamic Skill Loading: Route sub-agent findings through isolated file directories and load full skill instructions only when relevant. - Use Case: A web search returns 8000 tokens of results. Instead of flooding the conversation, the agent writes the output to a scratch file, keeps a 100-token summary in context, and greps the file later for specific details. ## Quick Start Ask the agent to offload large tool outputs to scratch files and persist the current task plan to disk so it can reload context on demand.
