Faster

A Python CLI tool that fetches workout data from intervals.icu and provides intelligent analysis using AI through OpenRouter.

Features

• Fetches training data directly from intervals.icu API
• AI-powered analysis using OpenRouter (supports multiple models)
• Analyzes fitness metrics, training load, and performance trends
• Natural language queries - just ask questions in plain English
• Fast and lightweight - no local database needed

Setup

1. Install dependencies

pip install -r requirements.txt

2. Configure credentials

Copy the example environment file and add your credentials:

cp .env.example .env

Then edit .env with your credentials:

INTERVALS_API=your_intervals_api_key
ATHLETE_ID=your_athlete_id
OPENROUTER=your_openrouter_api_key
OPENROUTER_MODEL=google/gemini-2.5-flash

Where to get credentials:

• intervals.icu: Log in → Settings → API Key
• OpenRouter: https://openrouter.ai (sign up for API access)

3. Run setup check

python faster.py --setup

Usage

Basic Usage

python faster.py "your question here"

Examples

# General training analysis
python faster.py "How's my training this month?"

# Specific activity analysis
python faster.py "Analyze my last 5 runs"

# Fitness metrics
python faster.py "What's my current fitness level?"

# Performance trends
python faster.py "Compare my interval sessions this week"

# Training advice
python faster.py "What should I focus on to improve my 10K time?"

# Recovery check
python faster.py "Am I overtraining?"

Advanced Options

# Look back more days (default is 30)
python faster.py --days 60 "Compare my fitness over the last 2 months"

# Look back 90 days
python faster.py --days 90 "Show my training volume trends"

How It Works

1. Fetches Data: Retrieves your training data from intervals.icu (today + last 30 days by default)
2. Formats Context: Structures the data including:
   • Athlete profile (fitness, fatigue, form)
   • Recent activities with metrics
   • Wellness data (if available)
3. AI Analysis: Sends the data + your question to OpenRouter's AI
4. Smart Insights: Returns actionable analysis and recommendations

Data Fetched

The tool fetches:

• Athlete Profile: CTL, ATL, TSB, FTP, threshold pace
• Activities: Distance, duration, heart rate, power, training load
• Activity Types: Runs, rides, swims, etc.
• Wellness Data: Optional recovery metrics

Understanding Metrics

• CTL (Chronic Training Load / Fitness): 42-day weighted average of training load - your fitness level
• ATL (Acute Training Load / Fatigue): 7-day weighted average - recent training stress
• TSB (Training Stress Balance / Form): CTL - ATL
  • Positive TSB = Fresh/recovered
  • Negative TSB = Fatigued
• Training Load: Workout stress score (similar to TSS)
• FTP: Functional Threshold Power (cycling)
• Threshold Pace: Running pace at lactate threshold

Project Structure

faster/
├── faster.py                # Main CLI script
├── config.py                # Configuration loader
├── intervals_client.py      # intervals.icu API client
├── llm_analyzer.py          # OpenRouter/LLM integration
├── requirements.txt         # Python dependencies
├── .env                     # Your credentials (not in git)
└── README.md               # This file

Tips

• Be specific: "Analyze my last 3 interval workouts" works better than "analyze my training"
• Ask for advice: "What should I focus on?" gets recommendations
• Compare periods: Use --days to look at longer trends
• Check recovery: Ask about overtraining, fatigue, or form

Cost

• intervals.icu: Free API access for premium members
• OpenRouter: Pay-per-use (~$0.01-0.03 per query with Gemini Flash)

Troubleshooting

Configuration errors

python faster.py --setup

Missing credentials

Check your .env file has all required fields

API errors

• Verify your intervals.icu API key is valid
• Check your athlete ID is correct
• Ensure OpenRouter API key is active

Debug mode

python faster.py "query" --debug

Future Enhancements

• Activity type filtering (runs only, rides only)
• Date range parsing ("last week", "January")
• Export analysis to file
• Interactive REPL mode
• Wellness data integration
• Power curve analysis

License

MIT

Repository

https://github.com/vivek-dodia/faster