Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Reads local memory files to score an AI agent across five objective axes and outputs a four-part action plan: one proactive task, one deferred item, one avoidance rule, and one concrete deliverable. All processing is local, no network calls are made.
Uses countable signals from memory files (done markers, error logs, correction counts) rather than subjective self-rating, so scores reflect actual behavior rather than agent bias.
When to use it
- Auditing task completion rate after a long session
- Catching repeated tool failures logged in memory files
- Generating a proactive first task to run without a user prompt
- Writing an avoidance rule to stop a recurring bad pattern
- Scheduling a deferred job based on current memory state
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What should I focus on this session?
- 1Run agentic-compass.py to read daily and long-term memory files
- 2Score each axis by parsing markers and logs in those files
- 3Identify the weakest axis from the scored results
- 4Produce a four-part plan: proactive task, deferred item, avoidance rule, ship output
- 5Optionally write the plan to memory/agentic-compass.md with --write flag
Score: 3.0/5. Weakest axis: Completion Rate. Plan includes drafting a specific implementation, retrying deferred cron jobs, avoiding a named API during peak hours, and creating a prioritization document.