What is the Skill Evolution Tracker?

It is a specialized Claude Code skill used to monitor, document, and analyze how specific AI capabilities improve and change over time.

Can I use this to measure the impact of updates?

Yes, it includes dedicated operations to quantify effectiveness by tracking quality scores, usage frequency, and efficiency improvements before and after updates.

How does it handle versioning?

It follows semantic versioning (e.g., v1.0, v1.1) to maintain a clear history of updates, major changes, and patch fixes within the skill's documentation.

What are evolution patterns?

Evolution patterns are recurring trends in how skills mature, such as when they typically add quick references or when validation criteria become more specific.

Skill Evolution Tracker

Name: Skill Evolution Tracker
Author: adaptationio

byadaptationio

Productivity & Workflow

Tracks and analyzes the development lifecycle of AI skills through versioning, impact measurement, and pattern analysis.

About

The Skill Evolution Tracker is a specialized utility designed to document the lifecycle and growth of Claude skills. It enables developers to maintain detailed version histories, record the rationale behind specific changes, and quantify the effectiveness of improvements using data-driven metrics. By analyzing evolution patterns, users can gain insights into skill maturity and extract meta-learnings to optimize the development of future capabilities, ensuring that AI skills remain high-performing and well-documented.

Key Features

Detailed change documentation with rationale and impact notes
0 GitHub stars
Evolution pattern analysis to identify skill maturity trends
Automated version tracking using semantic versioning (SemVer)
Quantitative impact measurement for quality and efficiency
Standardized changelog generation and YAML metadata management

Use Cases

Documenting major version releases and feature updates for AI skills
Measuring the success of skill refinements through quality score comparisons
Identifying long-term development patterns to establish organizational best practices

About

Key Features

Detailed change documentation with rationale and impact notes
0 GitHub stars
Evolution pattern analysis to identify skill maturity trends
Automated version tracking using semantic versioning (SemVer)
Quantitative impact measurement for quality and efficiency
Standardized changelog generation and YAML metadata management

Use Cases

Documenting major version releases and feature updates for AI skills
Measuring the success of skill refinements through quality score comparisons
Identifying long-term development patterns to establish organizational best practices