fitclaw-public-core

What This Skill Does

The fitclaw-public-core skill serves as the foundational, privacy-first implementation of the FitClaw AI coaching methodology. Designed specifically for developers and users who want to deploy high-quality fitness coaching workflows without the risk of exposing sensitive user telemetry or production-level operational data, this package acts as a sanitized reference implementation. It encapsulates the core coaching logic—covering critical areas like initial onboarding, hydration tracking, nutritional advice, and structured training programming—into reusable, modular components. By separating the 'method' of coaching from the 'state' of the individual user, fitclaw-public-core provides a standardized framework that can be integrated into various AI environments while ensuring that no private records, credentials, or production-bound runtime configurations are leaked.

Installation

To install the FitClaw Public Core skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/beachanger/fitclaw-public-core

Ensure that you are connected to the main ClawHub repository. Once installed, the skill will initialize four primary modules: onboarding, hydration, nutrition, and training. You can verify the installation by running claw list to ensure all dependent modules are active and ready for prompt injection.

Use Cases

FitClaw Public Core is designed for scenarios where demonstrability is key. It is ideal for:

• Developers building health-oriented AI agents who need a proven starting template.
• Fitness enthusiasts wanting to test an AI-driven coaching workflow without linking personal health accounts.
• Educational demonstrations where the methodology of AI-led nutritional and training guidance needs to be shown without revealing real-world private user history.
• Rapid prototyping of new fitness coaching interfaces.

Example Prompts

1. "FitClaw, please guide me through the onboarding process for a beginner interested in building baseline cardiovascular fitness."
2. "Can you explain the recommended hydration strategy for someone training in a humid environment using the standard hydration module?"
3. "I have a target of 1800 calories per day; show me how the FitClaw nutrition guidance structures this into a daily meal plan structure."

Tips & Limitations

• Review-First Policy: Always review your specific implementation before moving from a staging environment to a public-facing one. This package contains only logic, not your user-specific data.
• Sanitization: Do not modify the source code of this package to include sensitive API keys or local database paths. Keep your production configurations stored in secure environment variables outside this repository.
• Scope: This is a framework. It provides the 'how,' but you must provide the context (e.g., specific user goals or fitness level) in your prompt for the AI to provide relevant results.
• Hard Boundary: If you find that this package is being used to store historical user performance logs, you are violating the intended privacy architecture. Keep user-specific history in a separate, encrypted layer.