A sophisticated logging utility that parses natural language workout data and inserts it into a structured database.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install workout-track
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install workout-track using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Workout Track?
The workout-track skill is a specialized tool designed to bridge the gap between intense physical training and digital data management. As a core component of the Openclaw Skills ecosystem, it allows developers and fitness enthusiasts to log complex strength training sessions using nothing but free-form text. By analyzing metrics such as weight, reps, RPE, and rest times, it ensures your progress is captured accurately without the friction of manual spreadsheet entry.
Built to integrate with the life_db database architecture, this skill provides a seamless bridge between an AI agent and your personal health records. It leverages advanced parsing to normalize exercise names and session data, making it a vital asset for anyone building a comprehensive quantified-self platform using Openclaw Skills.
Workout Track Use Cases
- Logging a full gym session after a workout using natural language descriptions.
- Tracking specific strength metrics like RPE and rest intervals for powerlifting programs.
- Centralizing bodyweight and weighted exercise data into a self-hosted sport database.
- Recording session metadata such as duration and subjective feeling for long-term health analytics.
How Workout Track Works
- The AI agent receives a natural language or structured description of a workout from the user.
- The skill parses the input to extract session-level metadata like date and duration, alongside detailed exercise arrays.
- A summary of the parsed data (exercises, sets, reps, and weights) is presented to the user for verification.
- Once confirmed, the skill triggers an execution command that sources environment variables and runs a Python script via the uv package manager.
- The data is securely inserted into the sport schema of the life_db database.
- The skill returns a formatted success message including a session ID and a motivation-based comment.
Workout Track Setup
To get started with this skill within your Openclaw Skills environment, follow these steps:
- Ensure the
uvtool is installed on your system path. - Configure your database credentials in the following file:
~/.openclaw/services/life-db/.env
- Deploy the
insert_workout.pyscript into your configured Openclaw scripts directory. - Enable the workout-track skill in your agent configuration.
Workout Track Data Schema & Taxonomy
The skill organizes data into two primary levels of the life_db sport schema:
| Level | Fields captured |
|---|---|
| Session | session_date, duration_min, feeling (1-10), notes |
| Exercise | exercise_name, sets, reps, weight_kg, rpe, rest_sec, order_in_session |
All exercise names are normalized to ensure consistency (e.g., "Bench Press") across different logged sessions.
Workout Track Advanced Features
- Intelligent parsing of free-form text into structured JSON payloads.
- Conditional feedback logic that provides hype or encouragement based on the session's feeling score.
- Support for bodyweight exercises by handling null weight values dynamically.
- Robust shell-escaping and environment sourcing for secure database interactions within the Openclaw Skills framework.
SKILL.md
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