todo-manager

Autonomous AI agent task management is automated by orchestrating the full lifecycle from creation to verification while preserving history. The workflow cycles through INIT, WORK, VERIFY, CORRECTION, and ERROR modes to progress tasks without manual micromanagement.

Tracking long-running project tasks across multiple sessions requires a persistent record that maintains status updates and audit trails. Integrating a scheduler handles recurring sessions, while a changelog keeps a continuous history of task transitions that is never deleted.

To start an automated task workflow, load the task management skill and create a .todo.md file with your initial tasks. This initializes the automated execution process, allowing the system to begin progressing tasks through their lifecycle.

Subtasks can be delegated to sub-agents autonomously using WORK_MODE. This mode progresses tasks without requiring user input, while VERIFY_MODE subsequently checks the quality of the completed work before finalizing the task status.

Autonomous task management enforces safe operation with strict task lifecycle rules and atomic commits. It maintains persistent error tracking alongside an automatic changelog to ensure safe execution and a complete audit trail for project workflows.

An autonomous task lifecycle is not suited for workflows requiring manual micromanagement or those lacking recurring execution needs. It is designed for scaling autonomous agent workflows and long-running experiments rather than simple, single-session task lists.