Weekly Fitness Review

A weekly fitness summary skill that pulls workout data from Apple Health, Fitbit, Garmin, or Strava exports and generates an actionable review with volume trends, recovery indicators, and personalized next-week training recommendations. Designed for anyone from casual exercisers to structured training plan followers.

Supported Platforms & Integrations

Platform	Setup Method	Auth Type	Notes
Apple Health	XML export or Shortcuts	Local file	Exports via Health app or automated Shortcut
Fitbit	Web API token	OAuth2	Pulls activity, sleep, and heart rate data
Garmin Connect	FIT file export or API	OAuth2	Supports running, cycling, swimming, strength
Strava	API token in config	OAuth2	Activity feed with segment and effort data
Whoop	API token	OAuth2	Recovery score and strain data integration
Google Sheets	Service account	OAuth2	Logs weekly summaries for long-term trend tracking

When to Use This Skill

• Use this when you work out regularly but never look at your data to spot trends or overtraining
• Use this when you want a consolidated view across multiple fitness platforms in one place
• Use this when you are following a training plan and need weekly volume and intensity checks
• Use this when you suspect you are overtraining but lack objective data to confirm it
• Use this when you want week-over-week comparisons without manually building spreadsheets
• Use this when you need recovery recommendations based on sleep quality and training load
• Consider alternatives when you need real-time coaching during a workout — use your watch or a coaching app
• Consider alternatives when you need a full periodized training plan — consult a coach or use TrainingPeaks

Quick Start

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# ~/.claude/skills/weekly-fitness-review.yaml
primary_platform: apple_health
export_path: ~/Health/export.xml
review_day: monday
activities_tracked:
  - running
  - strength_training
  - cycling
  - yoga
goals:
  weekly_active_minutes: 200
  weekly_strength_sessions: 3
  weekly_cardio_sessions: 3
  rest_days_minimum: 1
sleep_tracking: true
resting_hr_tracking: true

First Run Example

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claude skill weekly-fitness-review --week 2026-03-09

Expected Output

Weekly Fitness Review — March 9-15, 2026
=========================================

Activity Summary:
  Total active minutes: 245 (goal: 200) [+23% vs last week]
  Workouts completed: 6
  Rest days taken: 1 (Sunday)

  Running:    3 sessions | 18.4 mi | Avg pace 8:32/mi | Avg HR 148
  Strength:   2 sessions | 95 min  | Upper + Lower split
  Yoga:       1 session  | 45 min  | Recovery flow

Recovery Indicators:
  Avg resting HR: 54 bpm (baseline: 52) -- slightly elevated
  Avg sleep: 7.1 hrs (target: 7.5) -- below target
  Sleep quality score: 78/100

Trends (4-week rolling):
  Running volume:  +12% (watch for overuse)
  Strength volume: Stable
  Recovery score:  Declining slightly

Next Week Recommendations:
  1. Add one extra rest or active recovery day
  2. Cap long run at 7 miles (easy pace) to manage volume increase
  3. Prioritize sleep — aim for 7.5+ hours Sun-Thu
  4. Consider deload on strength (reduce sets by 25%)

Overall Week Grade: B+ (strong volume, recovery needs attention)

Advanced Configuration

Platform-Specific Setup

Apple Health Export

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apple_health:
  export_path: ~/Health/export.xml
  auto_export_shortcut: "Weekly Health Export"
  metrics:
    - active_energy
    - exercise_minutes
    - heart_rate
    - sleep_analysis
    - resting_heart_rate
    - vo2_max

Strava API

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strava:
  enabled: true
  client_id: "your-client-id"
  access_token: "your-access-token"
  include_segments: true
  include_suffer_score: true
  activity_types:
    - Run
    - Ride
    - Swim

Full Options Reference

Parameter	Type	Default	Description
primary_platform	string	apple_health	Main data source for workout data
export_path	string	~/Health/export.xml	Path to health data export file
review_day	string	monday	Day of week to generate the review
activities_tracked	list	all	Activity types to include in analysis
goals.weekly_active_minutes	int	150	Target active minutes per week
goals.weekly_strength_sessions	int	2	Target strength training sessions
goals.weekly_cardio_sessions	int	3	Target cardio sessions per week
goals.rest_days_minimum	int	1	Minimum rest days enforced per week
sleep_tracking	bool	true	Include sleep duration and quality analysis
resting_hr_tracking	bool	true	Track resting heart rate trends for recovery
rolling_window_weeks	int	4	Weeks of history for trend calculations
overtraining_alerts	bool	true	Warn when volume increases exceed safe thresholds
output_format	string	terminal	terminal, markdown, or google_sheets
units	string	imperial	imperial or metric for distances and weights

Core Concepts

Concept	Purpose	How It Works
Training Load	Quantifies weekly stress	Combines duration, intensity (HR zones), and session count into a single load score
Recovery Score	Detects overtraining early	Tracks resting HR trend, sleep quality, and subjective fatigue indicators
Volume Progression	Safe improvement rate	Flags when weekly running or lifting volume increases more than 10% over prior week
Activity Balance	Prevents imbalanced training	Checks ratio of cardio to strength to flexibility and suggests adjustments
Trend Analysis	Spots patterns over time	Uses 4-week rolling averages to show whether fitness markers are improving or declining

Architecture

Data Sources (Apple Health / Fitbit / Garmin / Strava)
                    |
                    v
          +-------------------+
          | Data Normalizer   |----> Converts all formats to unified schema
          +-------------------+
                    |
                    v
          +-------------------+
          | Metrics Engine    |----> Calculates load, volume, recovery
          +-------------------+
                    |
          +---------+---------+
          |                   |
          v                   v
  +---------------+   +------------------+
  | Trend Tracker |   | Recommendation   |
  | (4-wk rolling)|   | Generator        |
  +---------------+   +------------------+
          |                   |
          v                   v
  +-------------------------------+
  | Weekly Report Formatter       |
  +-------------------------------+

Workflow Examples

Scenario 1: Runner Training for a Half Marathon

Input: 6 runs logged this week, total 32 miles, race in 5 weeks

Processing:
  - Compares weekly mileage to 4-week average (28 mi avg)
  - Calculates 14% volume increase — above 10% safe threshold
  - Checks long run proportion (12 mi = 37.5% of total, ideal is <30%)
  - Reviews easy vs hard effort distribution

Output:
  Warning: Weekly mileage jumped 14% — risk of overuse injury
  Long run is 37.5% of volume (recommend keeping under 30%)
  Suggestion: Next week, cap total at 30 miles with 9-mile long run
  Easy/hard split: 72/28 (good — target is 80/20, slight adjustment needed)
  Race readiness: On track for 1:55-2:00 half marathon

Scenario 2: Strength Training Progress Check

Input: 3 strength sessions logged with exercise details and weights

Processing:
  - Tracks total volume (sets x reps x weight) per muscle group
  - Compares to previous week for progressive overload
  - Checks muscle group balance (push vs pull ratio)

Output:
  Total lifting volume: 42,800 lbs (+5% vs last week)
  Push/Pull ratio: 1.3:1 (slightly push dominant — add a pull session)
  Strongest progression: Deadlift +10 lbs this week
  Plateau detected: Bench press — same weight for 3 weeks
  Suggestion: Try 5x3 heavy sets on bench or add pause reps

Scenario 3: Recovery Week Detection

Input: Resting HR trending up for 10 days, sleep averaging 6.2 hours,
       subjective fatigue rating 7/10

Processing:
  - Resting HR 4 bpm above 30-day baseline (significant)
  - Sleep deficit accumulating (target 7.5 hrs, averaging 6.2)
  - Training load was high for 2 consecutive weeks

Output:
  RECOVERY ALERT: Multiple indicators suggest accumulated fatigue
  Recommended action: Deload week
    - Reduce all training volume by 40-50%
    - Replace one cardio session with yoga or walking
    - Prioritize 8+ hours sleep for the next 5 nights
    - Retest resting HR at end of deload week
  If resting HR returns to baseline, resume normal training

Best Practices

1. Export data consistently on the same day — Set a recurring reminder to export your health data every Sunday evening or automate it with Apple Shortcuts. Consistent timing ensures accurate week-over-week comparisons.

2. Track resting heart rate every morning — The single most reliable indicator of recovery status. A sustained rise of 3+ bpm above your baseline warrants a lighter training week regardless of how you feel.

3. Do not chase volume numbers blindly — The review shows volume trends to help you progress safely, not to create pressure. A down week is often more valuable than an up week for long-term fitness.

4. Combine objective and subjective data — Add a brief energy rating (1-10) to your weekly check-in. Sometimes you feel terrible despite good metrics, and that matters as much as the numbers.

5. Review monthly trends, not just weekly snapshots — Individual weeks fluctuate. The 4-week rolling trend tells the real story about whether your fitness is improving.

Common Issues

1. Apple Health export file is too large to parse — If your export exceeds 1 GB, use the date_range parameter to limit parsing to the last 90 days instead of all-time history.

2. Heart rate data missing for some workouts — Ensure your watch was worn and properly fitted. The review will note sessions without HR data and exclude them from zone analysis.

3. Strength exercises not categorized correctly — Use the exercise_mapping config to map your custom exercise names to standard categories (push, pull, legs, core) so the balance analysis works accurately.

Privacy & Data Handling

All fitness data processing occurs entirely on your local machine. Health exports are read from your specified file path and parsed in memory — no copies are made or cached. Resting heart rate, sleep data, and workout details are stored only in your local ~/.fitness-review/history.json file for trend analysis. No health data is ever transmitted to external servers. If Google Sheets logging is enabled, data is written only to your own spreadsheet using your own credentials. API tokens for Strava, Fitbit, or Garmin are stored in your local Claude configuration and are never shared with third parties. You can delete all stored history at any time by removing the local history file.