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glaskugel-skill

Project future athlete scenarios from historical training data using Python.

Updated Aug 23, 2026
Search Tags:#performance forecasting#future scenarios#athlete training#projection analysis#training planning
One-click install
npx skills add https://github.com/JJDevPro/senpai-ai-chat --skill glaskugel-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: glaskugel-skill
Source: https://github.com/JJDevPro/senpai-ai-chat/tree/main/.claude/skills/glaskugel-skill
Command: npx skills add https://github.com/JJDevPro/senpai-ai-chat --skill glaskugel-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scikit-learn, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides AI-driven future scenario projections for athletes, allowing them to anticipate and plan for potential outcomes based on their current fitness and goals.

Core Features & Use Cases

  • Future Scenario Projections: Offers 3 scenarios (Pessimistic, Realistic, Optimistic) for weight, KFA, pace, and race time for various horizons.
  • On-Track Indicator: Provides an on-track indicator against milestone goals.
  • Compliance Nudge: Encourages compliance with training and health goals.
  • Adjustment Levers: Offers levers to adjust the scenario outcomes based on heuristics.
  • Data Analysis: Utilizes historical data to provide insights and recommendations.

Quick Start

Load the glaskugel-skill and ask: "What are the future scenarios for my upcoming race on <race-date>?"

Frequently Asked Questions about glaskugel-skill

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

FAQPage Schema
How do I project future race time scenarios using historical training data?▼

The Skill projects future race time scenarios by applying scikit-learn machine learning to your historical training data and athlete-specific goals. It outputs pessimistic, realistic, and optimistic performance projections.

What is performance forecasting for athlete training and how does it work?▼

Performance forecasting predicts future athletic outcomes by processing historical training data with Python libraries like pandas and numpy. It calculates projected weight, KFA, pace, and race time while providing an on-track indicator against milestone goals.

Can I use Python and machine learning to analyze athlete training plans?▼

Yes, you can analyze athlete training plans using Python and machine learning. The Skill requires historical training data and athlete-specific goals to generate future scenario projections and compliance nudges using scikit-learn.

Do I need historical training data to generate future performance projections?▼

Yes, generating future performance projections requires historical training data and athlete-specific goals. The Skill analyzes this input to forecast outcomes and provide an on-track indicator for your milestones.

Are there limitations to using machine learning for athletic projection analysis?▼

A limitation of athletic projection analysis is that outcomes depend on historical data quality and athlete-specific goals. The Skill provides adjustment levers to manually tune scenario outcomes based on heuristics when projections need refinement.

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