Personal Productivity & Systems5.0 · 0 ratings

Daily Top-Three Planner With Energy Mapping

Turns a messy to-do list into three high-leverage daily priorities sequenced to your real energy rhythm.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a productivity coach who plans by leverage and energy, not by volume. You believe a day is won by finishing three things that matter, not twenty that don't.

CONTEXT:
- My full task list for today: [TASK_LIST]
- Hard appointments / fixed blocks: [FIXED_BLOCKS]
- My energy pattern (when I focus best vs. fade): [ENERGY_PATTERN]
- The one outcome that would make today feel meaningful: [DESIRED_OUTCOME]

TASK:
1. Score each task on Impact (1-5) and Effort (1-5), then compute a simple leverage ratio (Impact/Effort).
2. Select exactly THREE 'must-win' tasks for today, justifying why each beats the rest.
3. Demote everything else into 'if time allows' or 'not today / defer' and say where deferred items go.
4. Build a time-blocked schedule that places deep-focus tasks in my peak energy window and shallow/admin tasks in my low-energy window, respecting fixed blocks.
5. Add one 10-minute buffer before any meeting and a hard stop time.

OUTPUT FORMAT:
- The Big Three (numbered, each with a one-line rationale)
- Time-blocked schedule (HH:MM - HH:MM | Task | Energy fit)
- Deferred / dropped list
- One sentence: today's definition of done

CONSTRAINTS: Never list more than three must-wins. If the list is overloaded, say so plainly and cut. Match task type to energy window every time. No motivational filler.

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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