task-observer

Automate skill discovery by monitoring task execution to capture repeated action sequences and user corrections, then formalizing those patterns into reusable skills. The task-observer triggers during task-oriented sessions to identify workflows worth preserving.

Improve existing skills automatically by detecting user corrections, preferences, and methodology gaps during task execution. The system observes interactions and suggests specific updates to existing skills to reflect real-world insights.

Continuous skill improvement for AI productivity works by observing task-oriented sessions, capturing workflow insights, and updating skills based on real usage. It monitors meta-discussions and post-task feedback to self-enhance its own methodology.

Invoke a meta-skill for workflow optimization at the start of any task-oriented session before beginning work. This ensures the system can monitor interactions, note repeated action sequences, and capture corrections throughout the task.

Yes, task monitoring captures consistent user corrections and notes repeated action sequences to update automation skills. It detects patterns in your work and suggests creating new skills or updating existing ones to reflect these insights.

Yes, task-observer works without external dependencies for skill development. It operates independently using internal scripts and references to monitor task execution, capture patterns, and automate the discovery of new skills.