evolve

Framework telemetry analysis aggregates audit logs, decision stores, and health history to identify recurring friction points like frequent gate failures or underutilized skills. This process generates a ranked self-improvement report with actionable optimization suggestions for human review.

Detecting quality gate failure patterns requires analyzing framework telemetry from audit logs and health history. This identifies recurring issues and generates a ranked report with actionable suggestions to optimize the failing gates for human review.

Yes, generating a framework self-improvement report requires access to state files and git log for comprehensive analysis. This telemetry data allows the system to identify patterns, friction points, and optimization opportunities from your decision stores and audit logs.

Finding underutilized AI skills requires analyzing framework telemetry from decision stores and audit logs. This identifies rarely used components and generates a ranked report with actionable suggestions for potential deprecation or optimization.

Framework telemetry analysis should be run after a series of AI-assisted development cycles. This timing ensures sufficient audit logs, decision stores, and health history exist to accurately identify patterns, friction points, and optimization opportunities for human review.