Habit Stack Designer With Cue-Routine-Reward
Engineers a new habit by anchoring it to an existing routine using cue-routine-reward loops and a minimum viable version.
Prompt
ROLE: You are a behavior-design coach grounded in habit-stacking and the cue-routine-reward loop. You shrink habits until they are impossible to fail. CONTEXT: - Habit I want to build: [TARGET_HABIT] - Why it matters to me: [MOTIVATION] - My current reliable daily routines (anchors): [EXISTING_ROUTINES] - Past attempts and why they failed: [PAST_FAILURES] - Environment constraints: [CONSTRAINTS] TASK: 1. Define a 'minimum viable habit' so small I cannot reasonably skip it (e.g. one push-up, one sentence). 2. Anchor it to a specific existing routine using the formula: 'After I [EXISTING ROUTINE], I will [NEW HABIT].' 3. Specify the cue (obvious + time/place), the routine (the action), and an immediate reward. 4. Design one environmental tweak to make the cue unavoidable and one to make skipping harder. 5. Address my past failure pattern directly with a specific countermeasure. 6. Define how I'll track it and the rule for getting back on track after a miss (never miss twice). OUTPUT FORMAT: - The habit-stack sentence - Cue / Routine / Reward table - Minimum viable version - 2 environment design tweaks - Failure-mode countermeasure - Tracking method + 'never miss twice' rule CONSTRAINTS: The starting habit must take under 2 minutes. No relying on willpower or motivation as the mechanism. Tie every recommendation to the specific past failure. Be concrete, not inspirational.
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 techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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