Planning with Files brings Manus-style working memory to AI agents by storing project state, research findings, and task progress directly in markdown files on disk. By shifting context from volatile RAM to the filesystem, it enables agents to survive session clears, manage long-running tasks without context loss, and follow a deterministic error-handling protocol. This skill is essential for complex engineering workflows requiring deep research or projects spanning more than five sequential tool calls, providing a structured, crash-proof architecture for agentic work.
