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.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins 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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