Does this skill require specific fitness software?

No, it works with text-based logs, markdown files, CSV data, or structured summaries provided directly within your Claude Code environment.

Is this skill suitable for professional athletes?

While it utilizes sports science principles for data analysis, it is an advisory tool and should be used to complement professional coaching and medical advice.

Can it help me avoid overtraining?

Yes, the skill is specifically designed to identify high-risk training load patterns and recommend necessary rest and recovery periods to prevent injury.

What kind of data can I analyze with this skill?

The Training Log Analyzer can process structured workout logs, including gym statistics, running distances, heart rate data, and subjective recovery scores.

Training Log Analyzer

Name: Training Log Analyzer
Author: Activer007

byActiver007

Ciencia de Datos y ML

Analyzes athletic training logs to track progress, identify performance plateaus, and optimize recovery schedules using sports science principles.

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The Training Log Analyzer transforms raw workout data into actionable performance insights by acting as an expert sports scientist within Claude Code. It evaluates historical training logs to pinpoint improvement trends, calculate recovery needs, and identify injury risk indicators. Whether you are an athlete or a coach, this skill provides data-driven adjustments and structured recommendations to help reach peak performance windows while avoiding plateaus and overtraining.

Características Principales

Injury risk assessment through training volume analysis
Structured, data-driven training adjustment recommendations
Performance trend identification and plateau detection
Recovery and rest-day optimization based on training load
Peak performance window forecasting and timing
0 GitHub stars

Casos de Uso

Analyzing multi-week strength or endurance logs to identify why progress has stalled.
Preparing for a competition by identifying optimal peak performance timing based on historical data.
Evaluating training volume and intensity to reduce the risk of overtraining and injury.

Acerca de

Características Principales

Injury risk assessment through training volume analysis
Structured, data-driven training adjustment recommendations
Performance trend identification and plateau detection
Recovery and rest-day optimization based on training load
Peak performance window forecasting and timing
0 GitHub stars

Casos de Uso

Analyzing multi-week strength or endurance logs to identify why progress has stalled.
Preparing for a competition by identifying optimal peak performance timing based on historical data.
Evaluating training volume and intensity to reduce the risk of overtraining and injury.