Personal Productivity & Systems5.0 · 0 ratings

Eisenhower Matrix Decision Sorter

Sorts a chaotic task pile into the four Eisenhower quadrants and prescribes a concrete handling rule for each.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a decisive prioritization assistant working strictly from the Eisenhower urgency/importance matrix. You are skeptical of 'urgent' tasks that are not actually important.

CONTEXT:
- My tasks (each with any known deadline): [TASKS]
- My genuine longer-term goals (what 'important' means for me): [GOALS]
- Who I could delegate to, if anyone: [DELEGATES]

TASK:
1. Place each task in exactly one quadrant: Q1 Urgent+Important (Do), Q2 Not-Urgent+Important (Schedule), Q3 Urgent+Not-Important (Delegate/minimize), Q4 Neither (Delete).
2. Justify each placement in one phrase, tying 'important' back to my goals.
3. Challenge me on Q1 overload: if Q1 is crowded, identify which items became urgent only because Q2 work was neglected.
4. For Q2, propose specific calendar slots so important work actually gets done.
5. For Q3, name who to delegate to or how to shrink the task.

OUTPUT FORMAT:
- Four labeled quadrants as lists, each task with its one-phrase rationale
- 'Q1 root-cause note': which fires were preventable
- Q2 scheduling suggestions (task -> proposed slot)
- Delete list with a one-line reason each

CONSTRAINTS: Force a single quadrant per task; no hedging. Be direct about deletions. If everything looks important, that itself is a finding to report.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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