training-load

Calculate CTL, ATL, and TSB from local Strava training data.

Updated Apr 12, 2026
One-click install
npx skills add https://github.com/AlvaroLaraFF/strava-coach --skill training-load
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: training-load
Source: https://github.com/AlvaroLaraFF/strava-coach/tree/main/.claude/skills/training-load
Command: npx skills add https://github.com/AlvaroLaraFF/strava-coach --skill training-load

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Compute PMC metrics (CTL/ATL/TSB) from your training history to quantify fitness, fatigue, and readiness, turning raw activity data into actionable insight.

Core Features & Use Cases

  • PMC calculation derives CTL, ATL, and TSB from daily training load using power-based TSS when watts are available or HR-based Banister TRIMP when not.
  • Current state & trends reports today’s PMC values, 7-day deltas, peak CTL, and lowest TSB to support race prep and recovery planning.
  • Use cases: suitable for endurance athletes tracking Strava data locally to make data-driven training decisions.

Quick Start

Run the training-load Python CLI to compute PMCs from your local activity history and inspect the resulting JSON.

Frequently Asked Questions about training-load

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate CTL, ATL, and TSB from my Strava training history?▼

To calculate CTL, ATL, and TSB from Strava data, run the training-load Python CLI against your local Strava database. It computes daily load using power-based TSS or HR-based Banister TRIMP, outputting current fitness, fatigue, and readiness values as JSON.

What is the difference between TSS and Banister TRIMP for training load calculation?▼

TSS and Banister TRIMP are two methods for quantifying training load. The Skill automatically uses power-based TSS when wattage data is available in your Strava activities, and falls back to HR-based Banister TRIMP when power data is absent to ensure accurate PMC calculations.

Do I need Python 3.10 or higher to compute PMC metrics locally?▼

Yes, you need Python 3.10 or higher to run the training-load CLI and compute PMC metrics. The script reads endurance activity data directly from your local Strava database without requiring external dependencies.

Can I use Strava activity data without power meter readings for fitness tracking?▼

Yes, you can use Strava data without power meter readings for fitness tracking. When watts are unavailable, the calculation automatically applies HR-based Banister TRIMP to your daily training load to derive accurate CTL, ATL, and TSB values.

What Strava training metrics are included in the JSON output for race prep?▼

The JSON output includes today’s CTL, ATL, and TSB values, 7-day deltas, peak CTL, and lowest TSB. These metrics from your Strava history support data-driven race preparation and recovery planning.

How do I track fitness and fatigue trends to optimize endurance training?▼

You track fitness and fatigue trends by analyzing the 7-day deltas, peak CTL, and lowest TSB computed from your local Strava activity history. This quantifies your current training state to optimize endurance performance and readiness.