autonomous-skill

To automate long-running tasks across multiple AI sessions, this Skill decomposes large goals into sequential steps and maintains progress logs. It uses an agent-based execution pattern to manage operations autonomously, even when interactions span different sessions.

Task decomposition for autonomous workflow execution is the process of breaking down complex objectives into manageable, sequential tasks. This Skill creates dedicated directories with task lists and progress logs to enable auto-continuation of intricate workflows.

Yes, you can use headless mode to execute complex codebase refactoring autonomously. The Skill manages multi-session execution through an initializer and executor agent pattern, tracking progress to ensure autonomous completion of extensive refactoring workflows.

You track progress for multi-session automated workflows by maintaining detailed task lists and progress logs. This Skill automatically creates and updates these files within dedicated directories to provide visibility and continuity for long-running operations.

Agent-based execution for extensive research tasks relies on file-based progress tracking and sequential task decomposition. It is designed for multi-session continuity, but requires dedicated directory management for logging and does not handle parallel task branches.