Energy Rhythm System — Minimal Specification

Core Concept

Time is energy. Activities either drain or restore your energy tank. The system tracks this balance over time to reveal your natural rhythms and predict sustainability.

INPUT

Data Source: Calendar Event Log

Standard format (iCal, Google Calendar export, or JSON):

{
  "events": [
    {
      "start": "2025-01-15T09:00:00",
      "end": "2025-01-15T10:30:00",
      "title": "Team Standup",
      "category": "meeting"
    }
  ]
}

Category Classification

Only 6 categories needed:

Auto-classification rules:

• Events with keywords “meeting”, “sync”, “call”, “1:1” → meeting
• Events with “focus”, “deep work”, “write”, “code” → work
• Events with “email”, “admin”, “review” → admin
• Events with “lunch”, “break”, “walk” → rest
• Time blocks with no events → vacant
• 10pm-7am with no events → sleep

ALGORITHM

Step 1: Time Discretization

Convert calendar to hourly slots over N days:

Day 1: [sleep][sleep][sleep][sleep][sleep][sleep][sleep][vacant][work][meeting][work][rest][work][work][admin][vacant][rest][vacant][vacant][sleep][sleep][sleep][sleep][sleep]
        00     01     02     03     04     05     06     07      08    09       10    11    12    13    14     15      16    17      18      19     20     21     22     23

Step 2: Energy Balance Calculation

energy = STARTING_ENERGY  # e.g., 100
energy_timeline = []

for hour in all_hours:
    category = get_category(hour)
    rate = RATES[category]
    energy = clamp(energy + rate, 0, MAX_ENERGY)
    energy_timeline.append(energy)

Key constraint: Energy has floor (0 = burnout) and ceiling (100 = fully charged).

Step 3: Rhythm Extraction

Daily rhythm (24-hour pattern):

daily_rhythm = [0] * 24
for day in days:
    for hour in range(24):
        daily_rhythm[hour] += energy_at(day, hour)
daily_rhythm = [x / num_days for x in daily_rhythm]  # average

Weekly rhythm (7-day pattern):

weekly_rhythm = [0] * 7
for week in weeks:
    for day in range(7):
        weekly_rhythm[day] += avg_energy_on(week, day)
weekly_rhythm = [x / num_weeks for x in weekly_rhythm]

Step 4: Sustainability Metrics

# Net energy change per week
weekly_delta = energy_end_of_week - energy_start_of_week

# Sustainability score
if weekly_delta >= 0:
    sustainability = "sustainable"
elif weekly_delta > -10:
    sustainability = "caution"
else:
    sustainability = "unsustainable"

# Time to burnout (if negative trend)
if weekly_delta < 0:
    weeks_to_burnout = current_energy / abs(weekly_delta)

# Recovery needed
deficit = MAX_ENERGY - current_energy
hours_to_full = deficit / REST_RATE

Step 5: Forecast

Project energy forward using:

1. Scheduled future events (known drains)
2. Assumed vacant time restoration
3. Historical weekly pattern (if no events scheduled)

for future_day in range(forecast_days):
    if has_scheduled_events(future_day):
        apply_scheduled_events()
    else:
        apply_historical_pattern(day_of_week)

OUTPUT

Visualization 1: Energy Tank

Current state at a glance.

┌─────────────────┐
│ ████████████░░░ │  78%
│                 │
│ ↑ +3 this week  │  sustainable
└─────────────────┘

Visualization 2: Energy Timeline

Past + future energy balance.

100 ┤
    │    ╭─╮      ╭─╮      ╭─╮
 75 ┤╭──╯  ╰─╮  ╭╯  ╰─╮  ╭╯  ╰──╮
    │        ╰──╯      ╰──╯      ╰─ forecast
 50 ┤                              ···········
    │
 25 ┤
    │
  0 ┼────────────────────────────────────────
    Mon  Tue  Wed  Thu  Fri  Sat  Sun  Mon...
         ↑ today

Visualization 3: Daily Rhythm (24h)

Average energy by hour.

     ▁▁▁▁▁▁▂▄▃▂▃▄▅▄▃▂▃▄▅▆▇█▇▅
     0  3  6  9  12 15 18 21 24

     Low: 9-11am (meetings drain)
     High: 8-10pm (recovered)

Visualization 4: Weekly Rhythm (7d)

Average energy by day of week.

Mon ████████░░░░  62%
Tue ██████░░░░░░  48%  ← lowest (heavy meeting day)
Wed ████████░░░░  65%
Thu ███████░░░░░  58%
Fri █████████░░░  72%
Sat ███████████░  92%  ← peak
Sun ██████████░░  85%

Visualization 5: Sustainability Dashboard

┌─────────────────────────────────────────────┐
│  WEEKLY ENERGY BALANCE                      │
├─────────────────────────────────────────────┤
│  Drained:    32 hours  │  Meetings: 12h    │
│  Restored:   38 hours  │  Work: 20h        │
│  ─────────────────────  │  Rest: 8h         │
│  Net: +6 hours          │  Sleep: 49h       │
│                         │  Vacant: 21h      │
│  Status: ✓ Sustainable  │                   │
└─────────────────────────────────────────────┘

Visualization 6: Rhythm Comparison

Show ideal vs actual patterns.

Ideal:   ▂▁▁▁▁▁▂▄▆▇▇▆▅▆▇▇▆▅▆▇███▇▅▂
Actual:  ▂▁▁▁▁▁▂▃▂▁▂▃▄▃▂▂▃▄▅▆▇█▇▅▂
                 ↑↑↑
              morning drain
              higher than ideal

IMPLEMENTATION NOTES

Simplifications That Preserve Effectiveness

1. Hourly granularity — Fine enough to capture patterns, coarse enough to be simple
2. 6 categories — Covers 95% of activities without complexity
3. Linear energy model — Avoids complex differential equations
4. No FFT — Simple averaging reveals daily/weekly rhythms
5. Auto-classification — Reduces manual input burden

What This Misses (Acceptable Tradeoffs)

• Event-specific intensity (all meetings drain equally)
• Context switching costs (could add later)
• Circadian variation (could weight hours differently)
• Social vs solo energy (introvert/extrovert modes)

Data Import Strategy

1. Google Calendar: Export as .ics, parse events
2. Outlook: Export as .csv or .ics
3. Apple Calendar: Export as .ics
4. Manual: Simple JSON format

All converge to the same internal format:

{ "start": "ISO8601", "end": "ISO8601", "title": "string", "category": "string" }

SUMMARY

The core insight: Track energy as a balance sheet, not just expenditure. Sustainability = income ≥ expenses over time.