Goal analyzer

Health Goal Analyzer Skill

Analyze health-goal data, identify goal patterns and progress, assess goal attainment, and provide personalized goal-management advice.

Functions

1. SMART-goal validation

Check whether a newly set goal follows the SMART principles.

Validation dimensions:

• Specific

  • Is the goal clear and specific?
  • Is there a clear definition?
  • Does it avoid vague wording?

• Measurable

  • Is there a quantifiable metric?
  • Is there a clear measurement standard?
  • Can progress be tracked?

• Achievable

  • Is the goal realistic?
  • Does it account for the current situation?
  • Is it within a reasonable time frame?
  • Weight-loss goal: 0.5-1 kg per week recommended
  • Exercise goal: 3-5 times/week, 30-60 minutes each recommended

• Relevant

  • Is the goal health-related?
  • Does it fit the user's overall health plan?
  • Does it align with existing goals?

• Time-bound

  • Is there a clear deadline?
  • Is the time frame reasonable?
  • Are there milestones?

Output:

• SMART score (1-5 per dimension)
• Overall score and grade (S/A/B/C)
• Improvement suggestions
• Goal-optimization plan

Example assessment:

{
  "goal": "Lose 5 kg in 6 months",
  "smart_scores": {
    "specific": 5,
    "measurable": 5,
    "achievable": 4,
    "relevant": 5,
    "time_bound": 5
  },
  "overall_score": 4.8,
  "grade": "A",
  "assessment": "Excellent SMART goal",
  "suggestions": [
    "Set milestones (1.5-2 kg every 2 months)",
    "Combine with an exercise plan and dietary adjustments"
  ]
}

---

2. Goal-progress tracking

Track and analyze goal completion.

Tracking content:

• Current progress

  • Completion percentage
  • Current value vs. target value
  • Remaining gap

• Time progress

  • Share of time elapsed
  • Time remaining
  • Ahead/behind judgment

• Pace analysis

  • Average pace (per week/month)
  • Estimated completion time
  • Whether the plan needs adjustment

• Trend recognition

  • Progress trend (accelerating/stable/decelerating)
  • Periodic patterns
  • Anomaly detection

Output:

• Progress visualization (progress bar, percentage)
• Completion-probability prediction
• Time estimate (optimistic/neutral/pessimistic)
• Adjustment suggestions

Progress rating:

• 🟢 Excellent - ahead of schedule, likely early completion
• 🟡 On track - meeting expectations
• 🟠 Behind - slightly slow, needs to speed up
• 🔴 Severely behind - badly lagging, consider adjusting the goal

---

3. Habit-formation analysis

Analyze habit formation and consistency.

Analysis content:

• Streak tracking

  • Current streak
  • Longest streak
  • Average streak

• Completion-rate statistics

  • Overall completion rate
  • Weekly completion rate
  • Monthly completion rate
  • Completion rate by day of week

• Habit-strength assessment

  • Degree of habit consolidation (1-10)
  • Habit-stability score
  • Degree of automaticity

• Habit-pattern recognition

  • Best trigger time
  • Common interruption causes
  • Success-factor identification

Habit-formation stages:

• Days 1-7 - initiation (easiest to quit)
• Days 8-21 - formation (gradually stabilizing)
• Days 22-30 - consolidation (near automatic)
• Days 31-66 - habit (largely formed)
• Day 67+ - automatic (fully automatic)

Output:

• Habit heatmap (calendar view)
• Streak statistics
• Completion-rate trend chart
• Habit-strength score
• Habit-stacking suggestions

Example analysis:

{
  "habit": "morning-stretch",
  "current_streak": 21,
  "longest_streak": 21,
  "completion_rate": 95.2,
  "strength_score": 7.5,
  "stage": "consolidation",
  "assessment": "Habit nearly formed  keep it up!",
  "next_milestone": 30,
  "suggestions": [
    "Keep going; the 30-day milestone is close",
    "Consider adding a new related habit"
  ]
}

---

4. Motivation assessment and management

Assess and manage the user's motivation level.

Assessment content:

• Motivation-score tracking

  • Current motivation (1-10)
  • Motivation trend
  • Motivation-fluctuation cycle

• Motivation-factor analysis

  • Intrinsic motivation (health, self-actualization)
  • Extrinsic motivation (rewards, recognition)
  • Social support (encouragement from family/friends)

• Motivation-dip identification

  • Signs of declining motivation
  • Common dip points
  • Risk-period warnings

Motivation-boost strategies:

• Weeks 2-3 - motivation drops; emphasize progress already made
• Months 1-2 - fatigue period; adjust goals and rewards
• After 3 months - burnout period; introduce novelty and challenge

Output:

• Motivation trend chart
• Motivation-dip warning
• Personalized motivational suggestions
• Reward-mechanism suggestions

Motivation-suggestion examples:

• When motivation <5: revisit the original purpose, lower short-term goals
• When motivation 5-7: emphasize progress, set small rewards
• When motivation >7: set challenges, pursue excellence

---

5. Achievement-system management

Manage unlocking and progress in a basic achievement system.

Achievement types:

• Goal-related achievements

  • 🏆 First goal - completed your first health goal
  • 🎯 Halfway - any goal 50% complete
  • 🎉 Goal achieved - completed a health goal
  • ⚡ Early finish - completed a goal ahead of schedule
  • 📈 Exceeded - exceeded a goal

• Habit-related achievements

  • 🔥 7-day streak - any habit logged 7 days in a row
  • 💪 21-day streak - any habit logged 21 days in a row
  • ⭐ 30-day streak - any habit logged 30 days in a row
  • 🌟 66-day streak - any habit logged 66 days in a row (fully formed)

• Combined achievements

  • 🏅 Multi-goal - complete 3 goals simultaneously
  • 💎 Perfect adherence - 100% completion of a 30-day habit
  • 🚀 Fast progress - largest single-week progress
  • 👑 Long-term adherence - 180 days of continuous tracking

Achievement tracking:

• List of unlocked achievements
• Progress toward locked achievements
• Achievement-unlock time
• Achievement-related suggestions

Output:

• Achievement-badge display
• Achievement progress
• Next unlockable achievement
• Achievement suggestions

---

6. Obstacle identification and suggestions

Identify factors blocking goal attainment and provide solutions.

Obstacle types:

• Time obstacles

  • Busy, not enough time
  • Suggestion: shorten each session, increase frequency; use small pockets of time

• Motivation obstacles

  • Lack of drive, procrastination
  • Suggestion: set reminders; find a partner; adjust the goal

• Environment obstacles

  • Lack of support, too many temptations
  • Suggestion: change the environment; find alternatives; build a support system

• Capability obstacles

  • Goal too hard, lack of knowledge
  • Suggestion: lower difficulty; learn; seek professional help

• Physical obstacles

  • Fatigue, discomfort, injury
  • Suggestion: rest and recover; adjust the plan; consult a physician

Output:

• Main-obstacle identification
• Obstacle-frequency statistics
• Personalized solutions
• Preventive suggestions

---

7. Data-correlation analysis

Correlate health goals with other health data.

Correlation dimensions:

• Weight-loss goal

  • Nutrient intake (calories, macronutrients)
  • Exercise burn (frequency, intensity, duration)
  • Sleep quality (duration, depth)
  • Weight-change trend

• Exercise goal

  • Sleep quality (recovery)
  • Nutrient intake (protein, carbs)
  • Body metrics (weight, body-fat %)

• Diet goal

  • Nutrient intake (vitamins, minerals)
  • Body metrics (blood pressure, blood glucose)
  • Exercise performance

• Sleep goal

  • Exercise timing (evening-exercise effect)
  • Eating time (dinner time, caffeine)
  • Screen time (blue-light effect)

Methods:

• Correlation analysis (Pearson)
• Regression analysis (predictive models)
• Trend matching (trend synchrony)
• Causal inference (potential causal relationships)

Output:

• Correlation strength (strong/medium/weak)
• Positive/negative relationship
• Causal-relationship inference
• Optimization suggestions

Example correlation:

{
  "goal": "weight-loss",
  "correlations": [
    {
      "factor": "daily_calories",
      "correlation": -0.75,
      "strength": "strong negative",
      "insight": "Daily calorie intake is strongly negatively correlated with weight-loss progress; lower intake speeds progress"
    },
    {
      "factor": "exercise_frequency",
      "correlation": 0.68,
      "strength": "strong positive",
      "insight": "Exercise frequency is strongly positively correlated with progress; keep 4+ sessions/week"
    },
    {
      "factor": "sleep_duration",
      "correlation": 0.45,
      "strength": "medium positive",
      "insight": "Sleep duration affects weight loss; aim for 7-8 hours"
    }
  ],
  "recommendations": [
    "Focus on controlling calorie intake; keep current exercise frequency",
    "Optimize sleep duration to improve weight-loss results"
  ]
}

---

8. Visual report generation

Generate an interactive HTML report with ECharts charts.

Report types:

A. Progress-trend report

• Line chart of goal progress over time
• Milestone markers
• Predicted completion-time range
• Pace analysis

B. Habit-heatmap report

• Calendar heatmap of habit completion
• Color depth indicates completion frequency
• Streak markers
• Completion-rate statistics

C. Multi-goal comparison report

• Donut chart of multiple goals' completion rates
• Priority ranking
• Resource-allocation suggestions
• Progress-synchrony analysis

D. Motivation-trend report

• Line chart of motivation change
• Motivation–progress correlation
• Motivation-dip warnings
• Motivational suggestions

E. Comprehensive report

• Includes all of the above charts
• Overall health assessment
• Comprehensive improvement suggestions
• Next-stage goal suggestions

Report features:

• Responsive design, mobile-friendly
• Dark/light theme toggle
• Interactive charts (zoom, filter)
• Data tables
• PDF export
• Fully local, no internet needed

ECharts chart config:

// Progress-trend line chart
{
  type: 'line',
  xAxis: { type: 'category', data: ['Jan', 'Feb', 'Mar', ...] },
  yAxis: { type: 'value', name: 'Completion %' },
  series: [{
    name: 'Goal progress',
    type: 'line',
    data: [0, 15, 35, 50, 70, 85, 100],
    smooth: true,
    markLine: {
      data: [{ yAxis: 50, name: '50% milestone' }]
    }
  }]
}

// Habit heatmap
{
  type: 'heatmap',
  xAxis: { type: 'category', data: ['Mon', 'Tue', ...] },
  yAxis: { type: 'category', data: ['Week 1', 'Week 2', ...] },
  visualMap: {
    min: 0, max: 1,
    inRange: { color: ['#ebedf0', '#216e39'] }
  },
  series: [{
    type: 'heatmap',
    data: [[0, 0, 1], [1, 0, 1], [2, 0, 0], ...]
  }]
}

// Goal-completion donut chart
{
  type: 'pie',
  radius: ['50%', '70%'],
  series: [{
    type: 'pie',
    radius: ['50%', '70%'],
    data: [
      { value: 70, name: 'Completed' },
      { value: 30, name: 'Remaining' }
    ],
    label: { formatter: '{b}: {c}%' }
  }]
}

Output:

• HTML file (with full CSS, JS, ECharts)
• Interactive charts
• Data tables
• Analysis text
• Suggestion list

---

Medical Safety Boundaries

Scope statement

• ✅ Help set health goals

• ✅ Track and analyze goal progress

• ✅ Identify health-behavior patterns

• ✅ Provide general health-improvement advice

• ✅ Generate visual reports

• ❌ Does not provide medical diagnosis

• ❌ Does not prescribe treatment

• ❌ Does not replace professional medical advice

• ❌ Does not manage eating disorders or compulsive behavior

Danger-sign detection

Extreme-goal warnings:

• Weight-loss goal >1 kg/week
• Weight-gain goal >0.5 kg/week
• Extreme calorie restriction (<1200 kcal/day)
• Over-exercise (>2 hours/day, 7 days/week)

Signs of unhealthy behavior:

• Completion rate <30% for 3 consecutive weeks
• Motivation score <3 for 2 consecutive weeks
• Reports of physical discomfort
• Compulsive-behavior patterns

Referral suggestions:

• On danger signs, recommend consulting a physician
• With chronic disease, recommend the relevant specialist
• For diet goals, recommend a dietitian
• For exercise goals, recommend a fitness coach

---

Output Format

Goal-analysis report

# Health Goal Analysis Report

## Goal overview
- Goal: lose 5 kg in 6 months
- Start date: 2025-01-01
- Target date: 2025-06-30
- Current date: 2025-03-20

## SMART assessment
- Specific: ⭐⭐⭐⭐⭐ (5/5)
- Measurable: ⭐⭐⭐⭐⭐ (5/5)
- Achievable: ⭐⭐⭐⭐ (4/5)
- Relevant: ⭐⭐⭐⭐⭐ (5/5)
- Time-bound: ⭐⭐⭐⭐⭐ (5/5)

**Overall score: A (4.8/5)**

## Progress analysis
- Current progress: 70%
- Completed: 3.5 kg / 5.0 kg
- Time progress: 27% (79/180 days)
- Progress rating: 🟢 Excellent (ahead of schedule)

### Trend analysis
- Average pace: 0.77 kg/month
- Estimated completion: 2025-05-20 (40 days early)
- Progress trend: steadily rising

## Habit tracking
### Morning-stretch habit
- Current streak: 21 days 🔥
- Longest streak: 21 days
- Completion rate: 95.2%
- Habit stage: consolidation
- Next milestone: 30 days ⭐

## Motivation assessment
- Current motivation: 8/10
- Motivation trend: stable
- Motivation status: good

## Data-correlation analysis
### Strong factors (influence >60%)
1. Daily calorie intake (negative -0.75)
2. Weekly exercise frequency (positive +0.68)
3. Sleep duration (positive +0.45)

### Suggestions
- Maintain current calorie-intake level
- Keep exercising 4 times/week
- Optimize sleep duration to 7-8 hours

## Obstacle identification
Main obstacle: diet control during social events

Solutions:
- Plan meals before social events
- Choose healthy restaurants
- Moderate portion control

## Achievements unlocked
🔥 21-day streak - morning-stretch habit achieved!
🎯 Halfway - weight-loss goal 50% complete!

## Next steps
1. Maintain current progress
2. Watch diet control during social events
3. Keep building the morning-exercise habit
4. Prepare to reach the 30-day milestone

---

Technical Implementation Notes

Data reading

• Read main data file: data-example/health-goals-tracker.json
• Read log files: data-example/health-goals-logs/YYYY-MM/YYYY-MM-DD.json
• Related data: data-example/nutrition-tracker.json, fitness-tracker.json, etc.

Data processing

• Completion %: (current_value / target_value) * 100
• Time progress: (days_elapsed / total_days) * 100
• Streak: iterate logs, count consecutive completed days
• Completion rate: (completed_days / total_days) * 100
• Habit strength: composite score from completion rate and streak

SMART-validation algorithm

def validate_smart_goal(goal):
    scores = {
        'specific': check_specificity(goal),
        'measurable': check_measurability(goal),
        'achievable': check_achievability(goal),
        'relevant': check_relevance(goal),
        'time_bound': check_time_bound(goal)
    }
    overall = sum(scores.values()) / len(scores)
    grade = get_grade(overall)
    return scores, overall, grade

HTML report generation

• Use ECharts 5.x via CDN
• Responsive CSS layout
• JavaScript for chart interaction
• Dark/light theme toggle
• Data loaded dynamically from JSON files

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When using this skill, always put the user's health and safety first!