A personal AI coaching skill that connects health data, fitness goals, and training reflections to your AI agent via the trainedby.ai MCP server.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install trainedby-mcp
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 trainedby-mcp 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 trainedby-mcp Personal AI Coach?
The trainedby-mcp skill is a specialized integration designed to turn your AI agent into a data-driven personal health coach. By connecting to the trainedby.ai infrastructure, it allows the agent to access, summarize, and search through your personal training history and health metrics. This tool is part of the expanding ecosystem of Openclaw Skills that bridge the gap between static health tracking and active, conversational coaching.
Whether you are logging a workout or reviewing monthly progress, this skill provides the context necessary for an AI to offer meaningful feedback. It leverages semantic search and vector embeddings to ensure that even long-past goals or specific reflections remain accessible to your agent, fostering a coaching relationship that grows more personalized over time.
trainedby-mcp Personal AI Coach Use Cases
- Logging daily workout feedback and reflections to build a comprehensive training history.
- Analyzing fitness trends over specific timeframes like weeks or months to see progress.
- Conducting a structured onboarding process to define fitness profiles and priorities.
- Searching through past health notes using semantic queries to find specific insights.
- Setting and tracking long-term wellness goals with proactive agent encouragement.
How trainedby-mcp Personal AI Coach Works
- The agent connects to the trainedby.ai MCP server and initiates authentication via Supabase OAuth.
- The whoami tool is used to verify the user's connection and data access permissions.
- The agent retrieves recent activity using the get_timeline tool to understand the user's current status.
- When the user provides new information, the save_note tool categorizes and stores it as a goal, workout, or reflection.
- For complex queries, the search_timeline tool uses vector embeddings to find relevant past entries across the user's history.
- Onboarding tools are triggered to fill gaps in the user profile using weighted importance scales.
trainedby-mcp Personal AI Coach Setup
To deploy this skill within your environment, configure your agent to connect to the following MCP endpoint:
# MCP Server URL
https://trainedby.fastmcp.app/mcp
Authentication will be handled automatically via a browser-based Supabase OAuth flow during the initial connection. No manual API keys are required for the Openclaw Skills setup process.
trainedby-mcp Personal AI Coach Data Schema & Taxonomy
The skill manages information through a timeline-based schema that supports various granularities and note types.
| Item | Description |
|---|---|
| Note Types | goal, workout_feedback, reflection, general |
| Granularity | item, day, week, month, year |
| Onboarding | Weighted questions based on missing profile data |
| Search | Semantic vector-based retrieval |
All notes include timestamps and are indexed for rapid semantic searching to provide high-context coaching interactions.
trainedby-mcp Personal AI Coach Advanced Features
- Multi-period timeline retrieval (last 7 days, 5 weeks, or 12 months) for trend analysis.
- Semantic search capabilities that understand the intent behind health-related queries.
- Smart onboarding that pre-fills suggested answers based on existing timeline data.
- Proactive data collection triggers that prompt for reflections after detected activities.
- Integrated feedback system for real-time reporting of bugs or feature requests to the developers.
SKILL.md
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