Agent skill

smart-recommendations

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Install this agent skill to your Project

npx add-skill https://github.com/natea/ExoMind/tree/main/skills/smart-recommendations

SKILL.md

Smart Recommendations Skill

Purpose

AI-powered recommendation engine that analyzes patterns in your life data to provide intelligent suggestions for optimizing schedule, tasks, meetings, energy management, and goal achievement.

Core Capabilities

1. Schedule Optimization

  • Best Times for Tasks: Analyzes completion history to suggest optimal time blocks
  • Energy-Based Scheduling: Recommends high-focus tasks during peak energy periods
  • Context Switching Reduction: Suggests task batching by context/type
  • Buffer Time Insertion: Identifies where breathing room is needed
  • Deadline Alignment: Recommends when to start tasks based on historical velocity

2. Task Prioritization Intelligence

  • Eisenhower Matrix Auto-Assignment: Suggests urgency/importance quadrants
  • Impact Scoring: Estimates task value based on goals and dependencies
  • Effort Estimation: Predicts realistic time requirements from similar tasks
  • Procrastination Detection: Flags repeatedly postponed tasks with suggestions
  • Quick Win Identification: Highlights high-impact, low-effort opportunities

3. Meeting Optimization

  • Meeting Value Assessment: Analyzes if your presence is truly needed
  • Decline Suggestions: Recommends meetings to skip with reasoning
  • Reschedule Opportunities: Suggests better timing for recurring meetings
  • Meeting-Free Day Protection: Identifies days needing full focus time
  • Preparation Time Blocking: Auto-suggests prep time before important meetings

4. Focus Time Protection

  • Deep Work Block Suggestions: Recommends when to schedule uninterrupted time
  • Distraction Pattern Analysis: Identifies common interruption sources
  • Communication Batching: Suggests specific times for email/chat checking
  • Context Preservation: Recommends how to minimize task switching
  • Flow State Triggers: Identifies conditions that enable your best work

5. Energy Management

  • Break Reminders: Suggests breaks based on work intensity and duration
  • Energy Dip Detection: Identifies low-energy periods and suggests appropriate activities
  • Recovery Time Calculation: Recommends downtime after intense periods
  • Burnout Prevention: Flags unsustainable pace before it's too late
  • Ultradian Rhythm Alignment: Suggests work/rest cycles matching natural patterns

6. Goal Adjustment Intelligence

  • Progress Velocity Analysis: Compares actual vs planned progress
  • Goal Realism Scoring: Assesses if goals are achievable given current data
  • Milestone Recommendations: Suggests intermediate checkpoints
  • Pivot Suggestions: Recommends when to adjust or abandon goals
  • Resource Reallocation: Identifies where to shift time/energy

7. Habit Trigger Suggestions

  • Implementation Intentions: Suggests "if-then" habit triggers
  • Habit Stacking Opportunities: Recommends linking new habits to existing ones
  • Environmental Cues: Suggests physical reminders and setup changes
  • Time-Based Triggers: Identifies best times for habit execution
  • Obstacle Removal: Recommends ways to reduce friction

Pattern Recognition

Data Sources Analyzed

yaml
schedule_patterns:
  - Task completion times and durations
  - Meeting attendance and outcomes
  - Calendar density and white space
  - Recurring event effectiveness

productivity_patterns:
  - High-output time periods
  - Task completion velocity
  - Procrastination triggers
  - Context switching frequency

energy_patterns:
  - Daily energy fluctuations
  - Break taking consistency
  - Recovery time needs
  - Burnout warning signs

goal_patterns:
  - Progress trajectory
  - Obstacle frequency
  - Effort distribution
  - Achievement rate

Recommendation Types

Immediate Actions

yaml
format: "RIGHT NOW: [Action]"
examples:
  - "RIGHT NOW: Take a 10-minute break - you've been in flow for 2.5 hours"
  - "RIGHT NOW: Decline the 3pm standup - no action items for you this sprint"
  - "RIGHT NOW: Block tomorrow 9-11am for deep work - your highest energy window"

Daily Suggestions

yaml
format: "TODAY: [Suggestion]"
examples:
  - "TODAY: Start the budget proposal now - historically you need 3 hours for financial docs"
  - "TODAY: Move the client call to afternoon - you're 40% more patient after lunch"
  - "TODAY: Batch all email responses to 4-4:30pm window"

Weekly Strategies

yaml
format: "THIS WEEK: [Strategy]"
examples:
  - "THIS WEEK: Protect Tuesday for focused work - only 2 meetings currently scheduled"
  - "THIS WEEK: Reduce meeting load by 3 hours - you're at 75% meeting time vs 60% target"
  - "THIS WEEK: Reschedule Friday 1-on-1s to Thursday - Friday energy typically drops"

Strategic Insights

yaml
format: "INSIGHT: [Pattern]"
examples:
  - "INSIGHT: You complete 3x more tasks when starting before 10am"
  - "INSIGHT: Monday meetings result in 50% more action items than other days"
  - "INSIGHT: Your creative work quality peaks Tuesday/Wednesday mornings"

Implementation

When to Request Recommendations

bash
# Morning routine - get daily recommendations
"What should I focus on today?"
"Any schedule optimizations for today?"

# Weekly planning - get strategic suggestions
"What are my recommendations for this week?"
"How should I adjust my schedule this week?"

# Task planning - get prioritization help
"Which tasks should I tackle first?"
"What's the best time to work on [task]?"

# Meeting review - get optimization suggestions
"Which meetings should I decline this week?"
"How can I optimize my meeting schedule?"

# Energy management - get break/rest suggestions
"When should I take breaks today?"
"Am I at risk of burnout?"

# Goal review - get adjustment recommendations
"How are my goals tracking?"
"Should I adjust any goals based on my progress?"

Recommendation Confidence Levels

yaml
HIGH_CONFIDENCE:
  - Based on 10+ data points
  - Pattern repeated consistently
  - Strong statistical correlation
  - Example: "You're 85% more productive working on reports before 10am"

MEDIUM_CONFIDENCE:
  - Based on 5-9 data points
  - Pattern emerging but not fully established
  - Moderate correlation
  - Example: "You tend to complete creative tasks faster on Tuesdays"

LOW_CONFIDENCE:
  - Based on 2-4 data points
  - Hypothesis for testing
  - Weak but interesting correlation
  - Example: "You might be more focused after morning walks"

EXPERIMENTAL:
  - Based on general research/best practices
  - No personal data yet
  - Worth trying to gather data
  - Example: "Studies show 90-minute work blocks optimize focus"

Recommendation Categories

A. Schedule Recommendations

yaml
time_blocking:
  - Optimal task start times
  - Best meeting windows
  - Focus block placement
  - Buffer time insertion

batching:
  - Similar task grouping
  - Context consolidation
  - Communication batching
  - Administrative task blocks

protection:
  - Deep work preservation
  - Meeting-free days
  - Recovery time blocking
  - No-interruption periods

B. Task Recommendations

yaml
prioritization:
  - Impact vs effort scoring
  - Deadline urgency ranking
  - Dependency ordering
  - Quick win identification

execution:
  - Best time to start
  - Estimated duration
  - Required energy level
  - Optimal context

delegation:
  - Tasks to offload
  - Automation opportunities
  - Collaboration suggestions
  - Outsourcing candidates

C. Meeting Recommendations

yaml
attendance:
  - Meetings to decline
  - Optional vs required
  - Alternative participation (async)
  - Representative delegation

scheduling:
  - Better time slots
  - Shorter duration opportunities
  - Format changes (async, email)
  - Frequency adjustments

effectiveness:
  - Preparation suggestions
  - Agenda improvements
  - Follow-up automation
  - Outcome tracking

D. Energy Recommendations

yaml
breaks:
  - Optimal break timing
  - Break duration suggestions
  - Break activity ideas
  - Movement reminders

recovery:
  - End-of-day wind-down
  - Weekend recharge plans
  - Vacation timing
  - Sabbatical consideration

prevention:
  - Burnout warning signs
  - Overcommitment flags
  - Rest deficit alerts
  - Boundary violations

Integration with Life OS

Daily Planning Integration

yaml
morning_recommendations:
  - Review overnight insights
  - Adjust daily plan based on suggestions
  - Accept/defer/reject recommendations
  - Track recommendation accuracy

real_time_suggestions:
  - Pop-up notifications (optional)
  - Calendar blocks with reasoning
  - Task list reordering
  - Energy level adjustments

Weekly Review Integration

yaml
recommendation_review:
  - Accuracy assessment
  - Accepted vs rejected recommendations
  - Impact of followed suggestions
  - Pattern refinement feedback

learning_loop:
  - Update recommendation models
  - Incorporate new patterns
  - Adjust confidence levels
  - Refine algorithms

Goal Review Integration

yaml
goal_recommendations:
  - Progress trajectory analysis
  - Milestone adjustment suggestions
  - Resource reallocation ideas
  - Goal abandonment signals

quarterly_insights:
  - Major pattern discoveries
  - Successful recommendation types
  - Areas for improvement
  - New recommendation categories

Privacy and Control

Recommendation Settings

yaml
frequency:
  - real_time: Enable/disable live suggestions
  - daily: Morning recommendation summary
  - weekly: Sunday night strategy brief
  - monthly: Pattern insight report

categories:
  - schedule_optimization: true/false
  - task_prioritization: true/false
  - meeting_management: true/false
  - energy_management: true/false
  - goal_adjustments: true/false

notification_style:
  - subtle: Show in review only
  - moderate: Daily digest
  - active: Real-time suggestions
  - aggressive: Proactive interruptions

Data Privacy

  • All analysis happens locally
  • No external AI model calls for sensitive data
  • User can delete recommendation history
  • Opt-in for specific data sources
  • Export recommendation logs for review

Recommendation Quality Metrics

Track Effectiveness

yaml
metrics:
  acceptance_rate:
    - Percentage of recommendations followed
    - By category and confidence level

  outcome_tracking:
    - Did following recommendation improve results?
    - Task completion, energy levels, goal progress

  false_positives:
    - Recommendations that seemed good but weren't
    - Reasons for rejection

  missed_opportunities:
    - Patterns you noticed that weren't recommended
    - Gaps in recommendation coverage

Continuous Improvement

yaml
learning_mechanisms:
  - User feedback (thumbs up/down)
  - Outcome correlation analysis
  - A/B testing different recommendation types
  - Pattern discovery algorithms
  - Confidence level calibration

Example Recommendation Scenarios

Scenario 1: Morning Energy Optimization

DATA PATTERN:
- Task completion 40% higher before 10am
- Creative work quality highest 8-10am
- Deep work sessions most successful early morning
- Energy dips significantly after lunch

RECOMMENDATIONS:
HIGH CONFIDENCE:
✓ Schedule all creative work between 8-10am
✓ Block 8-11am for deep work (no meetings)
✓ Move recurring team standup to 2pm
✓ Batch email responses to afternoon

MEDIUM CONFIDENCE:
• Consider earlier wake time (7am vs 8am)
• Experiment with morning exercise before work
• Limit coffee to one cup before focus blocks

EXPERIMENTAL:
? Try cold shower before creative work
? Test 90-minute focus blocks vs 60-minute

Scenario 2: Meeting Overload

DATA PATTERN:
- 28 hours of meetings last week (70% of work time)
- Only 4 meetings had actionable outcomes
- 60% of meeting time spent listening to updates
- Productivity 3x lower on heavy meeting days

RECOMMENDATIONS:
HIGH CONFIDENCE:
✓ DECLINE: Daily standups Mon/Wed/Fri (contribute async)
✓ DECLINE: Project review meeting (get recording + notes)
✓ SHORTEN: Weekly 1-on-1s from 60min to 30min
✓ RESCHEDULE: Client calls to Tuesday/Thursday (better energy)

MEDIUM CONFIDENCE:
• Propose alternating weekly all-hands attendance
• Batch all internal meetings to Monday/Friday
• Request agenda + materials 24hrs before joining

ACTION PLAN:
1. This week: Decline 3 lowest-value recurring meetings
2. Next week: Test async participation for standups
3. Track: Productivity gain from reclaimed time

Scenario 3: Procrastination Pattern

DATA PATTERN:
- "Write Q4 proposal" rescheduled 7 times
- All writing tasks delayed until deadline pressure
- 3x more time spent on writing when under pressure
- Better quality when given 2-3 day buffer

RECOMMENDATIONS:
HIGH CONFIDENCE:
✓ START NOW: Block tomorrow 9-11am for proposal draft
✓ Break into smaller tasks (outline, draft sections, review)
✓ Set fake deadline 3 days before real deadline
✓ Schedule accountability check-in with peer

MEDIUM CONFIDENCE:
• Work on proposal in 25-minute Pomodoro sessions
• Change environment (coffee shop, library)
• Reward completion with something you enjoy

ROOT CAUSE ANALYSIS:
- Task feels overwhelming (need smaller chunks)
- Unclear requirements (need to clarify scope)
- Perfectionism (need to embrace "good enough" draft)
- Low interest (consider if this is right work)

PREVENTION:
→ For future writing tasks, start 5 days before deadline
→ Create proposal template to reduce cognitive load
→ Partner with colleague who enjoys writing

Scenario 4: Burnout Prevention

DATA PATTERN:
- Working 60+ hours/week for 4 consecutive weeks
- Zero days completely off in 3 weeks
- Sleep quality decreased 30%
- Completion rate dropped despite more hours
- Irritability and decision fatigue increasing

RECOMMENDATIONS:
HIGH CONFIDENCE:
✓ URGENT: Take full day off this weekend (no laptop)
✓ Reduce work hours to 45/week for next 2 weeks
✓ Decline all new commitments until recovery
✓ Schedule 7+ hours sleep nightly (track with alarm)

IMMEDIATE ACTIONS:
✓ Cancel tomorrow's optional meetings
✓ Delegate project X to team member
✓ Push deadline for proposal Y by 1 week
✓ Block Friday afternoon as "recovery time"

STRATEGIC CHANGES:
• Audit all commitments and eliminate 20%
• Establish "shutdown ritual" at 6pm daily
• Schedule weekly sabbath (full day off)
• Set up burnout early warning system

WARNING SIGNS TO MONITOR:
- Continued poor sleep
- Rising irritability
- Decreased task completion
- Physical symptoms (headaches, tension)
- Loss of interest in previously enjoyed activities

If burnout continues despite interventions:
→ Consider taking 1-2 week vacation
→ Discuss workload with manager
→ Evaluate if role/job is sustainable

Advanced Features

Predictive Recommendations

yaml
anticipatory_suggestions:
  - "Big deadline next month - start blocking focus time now"
  - "Travel next week - batch meetings before/after"
  - "Typical Q4 crunch coming - frontload important projects"
  - "Energy usually dips in winter - adjust expectations"

Comparative Analysis

yaml
compare_to:
  - Your past self (last quarter, last year)
  - Anonymized peer patterns (similar roles)
  - Research-based best practices
  - Your stated ideal schedule

insights:
  - "You're having 40% more meetings than last quarter"
  - "Your focus time is 2x higher than typical for role"
  - "Sleep pattern optimal compared to circadian research"

Scenario Testing

yaml
what_if_analysis:
  - "What if I decline recurring meeting X?"
  - "What if I shift wake time to 6am?"
  - "What if I batch all meetings to 2 days/week?"
  - "What if I work 4-day weeks?"

estimated_impact:
  - Reclaimed time calculation
  - Energy level prediction
  - Productivity change estimate
  - Goal progress trajectory

Success Metrics

Recommendation Adoption

  • % of recommendations viewed
  • % of recommendations accepted
  • Time to act on recommendations
  • Category-specific adoption rates

Impact Measures

  • Task completion rate change
  • Goal progress velocity change
  • Meeting time reduction
  • Focus time increase
  • Energy level improvement
  • Burnout risk reduction

System Learning

  • Recommendation accuracy over time
  • Pattern discovery rate
  • False positive reduction
  • User satisfaction scores
  • Recommendation diversity

Getting Started

  1. Enable Recommendations: Turn on recommendation engine in settings
  2. Configure Preferences: Set notification level and categories
  3. Build Data Foundation: 2-4 weeks of tracking for initial patterns
  4. Review First Recommendations: Start with daily digest
  5. Provide Feedback: Rate recommendations to improve accuracy
  6. Iterate: Adjust settings based on what's helpful vs noisy

Related Skills

  • Daily Planning - Apply recommendations during planning
  • Weekly Review - Assess recommendation effectiveness
  • Tracking Habits - Get habit optimization suggestions
  • Processing Inbox - Prioritization recommendations
  • Goal Setting - Goal adjustment recommendations

Smart recommendations transform data into actionable intelligence. Let patterns guide your optimization.

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