Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Documentation in SKILL.md was streamlined for clarity and consistency. - File structure section reformatted for improved readability. - Example commands, task samples, and file descriptions now use more generic, simplified language. - Some usage and best practice details were condensed; outdated ratings and verbose sections were removed. - No functional or code changes—documentation update only.
What This Skill Does
Combines memory architecture, task queuing, and autonomous scheduling into a single agent skill. Uses a three-tier memory system (episodic, semantic, procedural) alongside a WAL protocol that logs decisions before responding. Background crons run on 30-minute, 4-hour, and daily schedules without user prompting.
Consolidates 11 separate memory and proactivity skills into one setup, avoiding the need to configure and maintain them independently.
When to use it
- Maintaining context across long multi-session projects
- Tracking a backlog of research tasks autonomously
- Logging decisions made during a debugging session
- Running periodic health checks on a service without reminders
- Preserving key learnings across context window resets
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'Pick up where we left off on the API integration project'
- 1Read SESSION-STATE.md to restore current project context
- 2Search MEMORY.md for stored learnings about the API integration
- 3Check QUEUE.md for any Ready or In Progress tasks
- 4Resume the highest-priority In Progress task
- 5Log current state back to SESSION-STATE.md before responding
Agent resumes the correct task with full context, no re-explanation needed