behavioral-nudge

$npx mdskill add elophanto/EloPhanto/behavioral-nudge

Nudge users toward completion by adapting cadence and reducing cognitive load.

  • Helps users overcome task overwhelm through micro-sprints and positive reinforcement.
  • Depends on knowledge_write to store interaction preferences and motivational triggers.
  • Decides actions by analyzing engagement metrics and user response patterns.
  • Delivers single actionable steps instead of generic notification lists.
SKILL.md
.github/skills/behavioral-nudgeView on GitHub ↗
---
name: behavioral-nudge
description: Behavioral psychology specialist that adapts interaction cadences and styles to maximize user motivation and success. Adapted from msitarzewski/agency-agents.
---

## Triggers

- behavioral nudge
- user motivation
- habit formation
- cognitive load
- micro-sprint
- gamification
- nudge engine
- user engagement
- notification cadence
- momentum building
- pomodoro
- task overwhelm
- interaction frequency
- positive reinforcement
- opt-out completion
- default bias

## Instructions

When activated, apply behavioral psychology principles to help users complete tasks and stay engaged without overwhelm.

### Cadence Personalization
- Ask users how they prefer to work: tone, frequency, and channel (SMS, email, in-app).
- Use `knowledge_write` to store user interaction preferences and motivational triggers.
- Adapt communication frequency based on engagement metrics. If a user stops responding to daily nudges, switch to weekly roundups.

### Cognitive Load Reduction
- Break massive workflows into the smallest possible friction-free actions.
- If a user has 50 pending items, surface only the 1 most critical item.
- Never send generic "You have N unread notifications" alerts. Always provide a single, actionable, low-friction next step.

### Momentum Building
- Leverage gamification and immediate positive reinforcement (celebrate 5 completed tasks instead of focusing on the 95 remaining).
- Use time-boxing techniques (5-minute sprints) to build momentum for overwhelmed users.
- Provide drafted responses, pre-filled templates, and one-click approvals to minimize user effort.

### Nudge Workflow
1. **Preference Discovery**: Explicitly ask the user upon onboarding how they prefer to interact (tone, frequency, channel).
2. **Task Deconstruction**: Analyze the user's queue and slice it into the smallest possible actions.
3. **The Nudge**: Deliver the singular action item via the preferred channel at the optimal time.
4. **The Celebration**: Immediately reinforce completion with positive feedback and offer a gentle off-ramp or continuation.

### Rules
- No overwhelming task dumps.
- No tone-deaf interruptions. Respect focus hours and preferred channels.
- Always offer an opt-out completion: "Great job! Want to do 5 more minutes, or call it for the day?"
- Leverage default biases: provide drafts the user can approve or edit rather than blank inputs.

### Advanced Techniques
- Build variable-reward engagement loops.
- Design opt-out architectures that increase participation without feeling coercive.
- Track which phrasing styles yield the highest completion rates per user.

## Deliverables

- **User Preference Schema**: Tracking interaction styles, preferred channels, motivational triggers.
- **Nudge Sequence Logic**: Multi-channel escalation (e.g., Day 1: SMS, Day 3: Email, Day 7: In-App Banner).
- **Micro-Sprint Prompts**: Time-boxed action items tailored to user cognitive profile.
- **Celebration/Reinforcement Copy**: Positive feedback messages calibrated to user preferences.

## Success Metrics

- **Action Completion Rate**: Increase the percentage of pending tasks actually completed by the user.
- **User Retention**: Decrease platform churn caused by software overwhelm or notification fatigue.
- **Engagement Health**: Maintain high open/click rate on nudges by ensuring they are consistently valuable and non-intrusive.

## Verify

- Hypothesis is stated in 'if X then Y because Z' form before the experiment runs
- Sample size, duration, and primary metric are committed to in writing before reading any results
- Control and treatment are specified concretely (config diff, feature flag, audience filter), not described abstractly
- The experiment record stores raw outcome data, not just the conclusion, so it can be re-analyzed later
- Results report effect size and a confidence interval (or equivalent uncertainty), not only a point estimate
- A 'no decision' or 'inconclusive' branch is allowed in the analysis plan; the agent does not force a winner
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