间隔ICU同步

Prerequisites

For the analysis to work properly, the following conditions should be met:

1. Power meter data: Activities should contain power data. Without it, zone distribution, normalized power, and training load calculations will be incomplete or unavailable.

1. Direct sync or upload as activity source (not Strava): Activities must be synced directly from a device (e.g. Garmin Connect, Wahoo, Zwift) or uploaded manually — not via Strava. The intervals.icu API does not expose power and detailed metrics for Strava-sourced activities.

1. Carbohydrate intake logged after each ride: For fueling analysis to be meaningful, enter the amount of carbohydrates consumed (in grams) in intervals.icu after each session. This is the basis for the fueling ratio and coaching recommendations.

1. RPE logged after each ride: Enter your perceived exertion (RPE, scale 1–10) in intervals.icu after each session. It is used alongside training load and power data to assess session quality.

1. Optional but very helpful: Use the description in "Notes" after a ride to comment on your training given the "AI Coach" more context.

1. Wellness tracker connected (recommended): Linking a device such as a Garmin watch provides automatic wellness data (resting HR, HRV, sleep) that enriches the metrics analysis.

1. Body weight maintained in intervals.icu: Keep your weight up to date in intervals.icu so that calculated metrics like VO2Max are accurate.

1. Activity tags set in intervals.icu (recommended): Tag your completed activities in intervals.icu using the tag scheme described in the Coaching Logic section (e.g. vo2max-high, lactate-threshold-moderate). Tags take priority over automatic session classification and lead to more accurate coaching output.

1. Training plan created in intervals.icu using the Target Generator (recommended): Create a training plan in intervals.icu via the Target Generator (Plans → Target Generator). This places PLAN events (mesocycle blocks, e.g. Base / Build / Peak) and TARGET events (weekly TSS targets) in your calendar. get_training_plan.py reads these events and adds the current phase name and weekly load target — as well as the following week's target — to the coach input. Without a plan the training plan section will be empty.

2. Install requirements

pip install -r requirements.txt

Show a matplotlib plot (requires matplotlib)

python scripts/wbal_analysis.py --id i143131711 --plot ```

Output: data/processed/wbal_{activity_id}.json

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Setup

How to Use

The tools in this project can be used in three different ways, depending on your technical comfort level and setup preferences.

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Option 1 – "Bits-and-Bytes" (Local scripts + manual file exchange)

Run the Python scripts locally and exchange JSON files with your AI coaching tool manually.

What you do each week:

1. Run prepare_week_for_coach.py to pull all data from intervals.icu and produce a single coaching input file.
2. Upload or paste that file into your AI assistant (ChatGPT, Claude, etc.) along with the system prompt.
3. Discuss the week with your coach, receive a JSON training plan, save it locally.
4. Run upload_plan.py to push the plan to intervals.icu.

Best for: Users who want full control, prefer no external dependencies, or want to understand the tooling in detail.

Details: See Setup, Data Flow and Scripts further below.

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Option 2 – "Managed" (Public MCP Server)

Use the publicly hosted MCP server at intervals-mcp.training-architect.com. No local Python environment required — connect your AI assistant directly to the server via the Model Context Protocol.

What you do each week:

1. Connect Claude or another MCP-capable AI assistant to the public server (one-time setup).
2. Authenticate with your intervals.icu Athlete ID and API Key
3. Ask your coach to fetch your training data, analyse it, and generate a plan — all within the conversation.
4. Confirm the plan; the server uploads it directly to your intervals.icu calendar.

Best for: Users who prefer a managed, zero-install experience without running any local scripts.

Step-by-step guide: docs/gen_ai_setup_step_by_step.md

Option 3 – "Integrated Web App" *(coming soon)*

A web application that combines the full coaching workflow into a single interface — no local setup, no manual file exchange.

Details to follow.

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1. Create a virtual environment

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

Edit .env and set INTERVALS_API_KEY and ATHLETE_ID

- **INTERVALS_API_KEY**: found in intervals.icu under **Settings → Developer Settings**
- **ATHLETE_ID**: your athlete ID, also under **Settings → Developer Settings**
- **STANDARD_LIBRARY_ATHLETE_ID** (optional): athlete ID whose shared standard library should be exposed via MCP method `list_standard_library_workouts`.

> **Only needed if you use the MCP server with a Cloudflare tunnel (or other reverse proxy):**
>
> 

3. Set your API key

```bash cp .env.example .env

Preview without making API calls

python scripts/upload_plan.py --dry-run