Display status and progress of Gran Maestro requests and tasks.
npx skills add https://github.com/myrtlepn/gran-maestro --skill list-myrtlepn
Please help me install this Agent Skill. Skill: list Source: https://github.com/myrtlepn/gran-maestro/tree/main/skills/list Command: npx skills add https://github.com/myrtlepn/gran-maestro --skill list-myrtlepn
This Skill provides a clear overview of all ongoing and completed AI requests and tasks, preventing confusion and ensuring no task is lost or forgotten.
/mst:list to quickly see which ones are still in the "PM Analysis" phase and which are actively being worked on by the AI team.Show me the status of all active requests.
High-intent search queries and answers about installing and using this skill.
Tracking active AI tasks involves filtering workflows by active status to view the current phase, task count, and progress. The Skill scans request JSON files or executes a Python script for real-time status updates on all ongoing requests.
Viewing progress on multiple AI workflows requires scanning request JSON files to display the current phase, task count, and status. This method provides a clear overview of all ongoing and completed requests, ensuring no task is lost or forgotten.
Yes, you can filter completed and active AI requests separately. The Skill supports filtering by active, completed, or all requests, allowing you to quickly see which workflows are still in the PM Analysis phase and which are finished.
To see which specific AI is working on a task, view the detailed task status provided by the Skill. It identifies whether an AI like Codex or Gemini is actively working on or waiting for each individual task within a request.
No, tracking AI task status does not require external dependencies. The Skill operates independently by scanning request JSON files or executing an internal Python script to retrieve real-time workflow updates.
AI task requests get lost or forgotten when there is no clear overview of ongoing and completed workflows. Using a status tracking command scans request JSON files to display task progress, preventing confusion and ensuring all tasks are monitored.
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