training-load
CommunityQuantify training load to optimize fitness.
AuthorAlvaroLaraFF
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: training-load Download link: https://github.com/AlvaroLaraFF/strava-coach/archive/main.zip#training-load Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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