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Mt weekly review

Skill MeisterLabs/meister-mcp-skills/skills/mt-weekly-review

Surface overdue tasks, tasks due this week, and recently completed tasks across MeisterTask projects for a weekly review.From its SKILL.md

Install
npx -y skills add MeisterLabs/meister-mcp-skills --skill mt-weekly-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

1.3 KB, 257 tokens by cl100k_base, as published. Nobody here has run it

Weekly review

Use the MeisterTask MCP to run a structured weekly review across your MeisterTask projects.

Workflow

  1. Ask the user which projects to include — don't fetch all projects by default.
  2. Fetch tasks per project using mt_tasks_list:
    • Identify overdue tasks (due date in the past, status open)
    • Identify tasks due within the next 7 days
    • Identify tasks completed in the last 7 days
    • Note: mt_tasks_list can time out on projects with 200+ tasks — use mt_tasks_search with a due date range filter for those instead.
  3. Organize findings into three sections:
    • Overdue — past-due open tasks grouped by project
    • Due this week — upcoming tasks grouped by project
    • Completed last week — recently closed tasks grouped by project
  4. Flag any projects with no activity in the last 7 days.
  5. Offer to reschedule overdue tasks or create a Note to capture review notes.

Important

If the user has many projects, ask which ones to include before fetching everything. Keep the output scannable — bullet points grouped by project, not a wall of text.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 326,144. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.