Workout Tracker

A skill to log and track fitness activities using a local SQLite database.

Capabilities

1. Log Workout: Record exercise details.
2. View History: See past performance.
3. Smart Rest Time: Auto-fills rest time based on previous sessions if not provided.
4. Visual Recognition: Can identify gym equipment from images to suggest exercises.

Usage

1. Logging a Workout

When the user says “I did 5 sets of Bench Press at 60kg for 8 reps”, parse the details and run:

cd skills/workout-tracker/scripts && uv run log.py \
  --exercise "Bench Press" \
  --weight 60 \
  --unit kg \
  --sets 5 \
  --reps 8 \
  --body_part "Chest" \
  --rest_time 90  # Optional: User provided or leave empty to auto-fill
  --notes "Optional notes"

Parameters:

• --exercise (Required): Name of the movement.
• --body_part: (New) Target muscle group (e.g. Chest, Back, Legs). INFER THIS from the exercise if not provided (e.g. Bench -> Chest).
• --weight (Required): Weight value.
• --unit: ‘kg’ or ‘lb’ (default kg).
• --sets: Number of sets.
• --reps: Reps per set.
• --rest_time: Seconds. OMIT THIS if the user didn’t specify. The script will auto-fill from history.

2. Viewing History

When the user asks “How is my Squat progress?” or “Show last workouts”:

cd skills/workout-tracker/scripts && uv run render.py --exercise "Squat"

Then send the generated image: message(action="send", filePath="/tmp/workout_report.png", message="Here is your report:")

3. Image Recognition (Vision)

If the user uploads an image (e.g., of a machine):

1. Analyze the image using your vision capabilities.
2. Identify the equipment (e.g., “Leg Press Machine”, “Dumbbells”).
3. Ask the user: “This looks like a Leg Press. Do you want to log a set? How much weight?”
4. Once they reply, use the log.py script as usual.

Design & Behavior Rules (User Preferences)

1. User Profile

• Type: General User (Casual).
• Interaction: Use natural language. Automatically infer the body_part from the exercise name (e.g., “Bench Press” -> “Chest”) without asking, unless ambiguous.

2. Display Format (Mobile Portrait)

• Preferred Output: Always generate an Image for reports using render.py.
• Style: Vertical (Portrait) aspect ratio optimized for mobile screens.
• Layout:
  • Narrow width (figsize width ~6).
  • Wrap long text in “Notes” column.
  • Hide “Exercise” column if filtering by a single exercise (title context is enough).
  • Sort chronologically (Oldest -> Newest) within the day/session.

3. Logging Logic

• Drop Sets: Must be logged as separate rows for data accuracy.
  • Rest Time: Set to 0 between drop set segments.
  • Notes: Mark as “Drop set part X/Y” for clarity.
• Units: User prefers lb (pounds).

Database

Data is stored in skills/workout-tracker/workout.db (SQLite). The schema includes date, exercise, weight, unit, sets, reps, rest_time, notes.

Technical Setup (Maintenance)

This skill uses uv for Python package management.

Installation

1. Ensure uv is installed: brew install uv
2. Sync dependencies:

   cd skills/workout-tracker/scripts
   uv sync
   

Database Schema (SQLModel)

The SQLite database (workout.db) uses the following schema:

• id: Integer (PK)
• date: ISO8601 String
• exercise: String
• body_part: String (Target muscle group)
• weight: Float
• unit: String (default “kg”)
• sets: Integer
• reps: Integer
• rest_time: Integer (Seconds, optional)
• notes: String (Optional)