Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
File-based memory system for AI agents that lose context between sessions. Implements a structured workspace with state files, boot sequences, and conflict resolution so agents can resume work reliably. Uses exact user quotes to prevent intent drift across context resets.
Most agent memory approaches rely on embeddings or summaries that lose precision; this system stores exact user instructions in plain files, making state auditable and trivially portable.
When to use it
- Resuming a multi-day coding task without re-explaining context
- Tracking blocked items across agent sessions
- Preserving decisions made during long research workflows
- Setting up a persistent workspace for a custom AI assistant
- Preventing repeated mistakes by logging lessons to disk
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'Set up memory for my agent and start tracking my refactor task'
- 1Copy template files from assets/templates/ to the workspace directory
- 2Write the user's exact words to state/ACTIVE.md with interpretation and steps
- 3Run the boot sequence: read HOLD.md, ACTIVE.md, DECISIONS.md, recent-work.md, MEMORY.md
- 4Output the boot status line showing active task, hold count, and staging count
- 5Begin work, logging decisions with timestamps to state/DECISIONS.md
Agent workspace initialized with active task tracked; subsequent sessions resume from the same state without re-prompting