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Manage AI agent tasks through approval-gated lifecycle stages with live updates.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "dum-e" yet — see the docs or source repo.
Create a task in dum-e for adding user login to an internal tool, set the initial status to todo, then suggest the steps to move it through in_progress, ai_testing, and manual_testing.
The tool returns the created task details and suggested actions or status updates for each lifecycle stage.
Before moving the current task from manual_testing to deployment, check whether human approval is required; if so, stop at the approval gate and ask for my confirmation.
It shows the current task status, whether human approval is triggered, and the next step after approval.
Use dum-e's interface to inspect my task list and summarize which tasks are in in_progress, ai_testing, and completed states.
It returns a status-grouped task overview so progress can be monitored quickly.
Developers can place work into a standardized lifecycle and track progress from todo to completed. Human approval gates help them intervene at critical checkpoints.
Solo developers or technical leads can use the CLI, REST API, and live updates to monitor task status in one place. This fits workflows that need structured progression and clear visibility.
When tasks enter AI testing, manual testing, or deployment, teams can use approval gates to control risk. It is useful where critical status changes require confirmation.
It is an MCP tool that lets AI agents manage tasks through a structured lifecycle. It provides a single-user task management system with a CLI, REST API, and live updates.
The provided information shows these states: todo, in_progress, ai_testing, manual_testing, deployment, and completed. It also includes human approval gates to control some transitions.
The provided material does not include installation steps, runtime requirements, or key information. For exact setup details, see the source repository.
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