Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Version 1.5.0 - Expanded and refined tags for improved discovery and categorization. - Updated data handling description for clarity on model usage and workspace trust boundaries. - Removed the disable-model-invocation flag from metadata. - Restructured and clarified documentation for installation, integration, and outputs. - No changes to command syntax, logic, or feature set.
What This Skill Does
Detects failures during agent sessions, records them as observations with recurrence, confirmation, and disconfirmation counters, and promotes patterns to constraints when evidence thresholds are met. All data stays in a local `.learnings/` directory with no external API calls.
Builds constraints from observed evidence rather than manually written rules, so patterns are only promoted when recurrence and confirmation thresholds are actually met.
When to use it
- Tracking repeated test failures across a project
- Capturing API timeout patterns during active development
- Searching past observations when a familiar error reappears
- Recording user corrections to agent behavior for pattern analysis
- Promoting recurring lint errors to agent-level constraints
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: The payment API keeps timing out, is this a known issue?
- 1Run /fm detect api with context 'Payment API 504 timeout'
- 2Check .learnings/observations/ for a matching observation
- 3Increment R counter on existing observation (payment-api-timeout)
- 4Run /fm classify to determine evidence tier based on R count
- 5Notify user the pattern is eligible for constraint generation (R≥3, C≥2)
Observation marked eligible; user prompted to run /ce generate to create a constraint from the pattern