A conversational AI integration for Strava that enables athletes to query performance trends, analyze fatigue, and generate structured workouts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install strava-skill
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 strava-skill 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 Strava AI Skill?
The Strava Skill provides a powerful bridge between your athletic data and AI agents within the Openclaw Skills ecosystem. By utilizing the Transition platform as a data sync layer, this skill allows users to talk to their Strava data as if they were speaking to a professional coach. It moves beyond simple activity logs by offering deep insights into fitness trends, personal records, and recovery needs.
This skill is designed for developers and athletes who want to build or use intelligent agents capable of understanding physical performance. From tracking cycling FTP progression to monitoring running mileage, the Strava integration ensures that your AI assistant has the full context of your training history to provide actionable advice.
Strava AI Skill Use Cases
- Querying weekly or monthly mileage trends using natural language.
- Monitoring cycling FTP and heart rate zone progression over time.
- Determining training readiness by analyzing fatigue and form (TSB) metrics.
- Generating random structured workouts for running, cycling, or swimming sessions.
- Comparing current performance against historical personal records (PRs).
How Strava AI Skill Works
- The skill connects to the Transition platform, which acts as the data aggregator for your Strava account.
- When a user asks a question, the AI Coach Chat endpoint processes the query with full context of the athlete's activity history.
- The skill retrieves specific performance metrics such as CTL (Fitness), ATL (Fatigue), and TSB (Form) to quantify training load.
- For tactical planning, the agent can fetch scheduled workouts or suggest new ones via the Workout of the Day (WOD) engine.
- The integrated agent then delivers a technical summary or recommendation based on the combined performance data.
Strava AI Skill Setup
To integrate this into your environment for Openclaw Skills, follow these steps:
- Download the Transition app and link your Strava account.
- Navigate to Settings > API Keys and select Generate New Key.
- Configure your environment variable:
export TRANSITION_API_KEY="tr_live_xxxxxxxxxxxxxxxxxxxxx"
Strava AI Skill Data Schema & Taxonomy
The skill organizes data into several high-level categories to facilitate AI analysis:
| Data Category | Description | Key Fields |
|---|---|---|
| PMC Stats | Performance Management Chart | CTL, ATL, TSB |
| Performance | Core athlete metrics | FTP, Threshold Pace, HR Zones |
| Activities | Historical training data | Sport Type, Duration, Intensity |
| Workouts | Future planning | Start/End Dates, Structured Steps |
| Profile | Identity & Settings | Athlete metadata and history |
Strava AI Skill Advanced Features
- Automated fatigue detection that warns users if TSB drops below a critical threshold (-20).
- Context-aware AI coaching that remembers previous chat history for consistent training advice.
- Multi-sport support allowing for a unified view of running, swimming, cycling, and strength training.
- Zero-authentication public endpoints for rapid workout generation without account overhead.
- High-frequency rate limits for paid tiers to support complex multi-agent data analysis.
SKILL.md
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