When is the best time to use the Feedback Analyzer?

It is best used after skills have been in active use for a period of time, allowing for enough usage data to identify meaningful trends and patterns.

What kind of metrics does Feedback Analyzer track?

It gathers metrics on usage frequency, build times, efficiency improvements, structural quality scores, and direct user feedback.

How does it measure the ROI of a skill?

It calculates ROI by comparing the time invested in building and maintaining a skill against the total hours saved during execution compared to manual baselines.

Can it help improve specific coding skills?

Yes, by identifying where efficiency plateaus or where quality issues arise, it provides specific recommendations for data-driven improvement decisions.

Feedback Analyzer

Name: Feedback Analyzer
Author: adaptationio

byadaptationio

分析と監視

Evaluates the impact and effectiveness of AI skills through data-driven usage metrics and ROI analysis.

概要

Feedback Analyzer is a specialized utility designed to quantify the value of your AI automation toolkit. By gathering data on usage frequency, build times, and efficiency gains, it provides a comprehensive overview of how well specific skills are performing. It moves beyond qualitative assessment by calculating concrete ROI metrics, identifying long-term usage trends, and extracting actionable insights. This skill is essential for teams looking to optimize their Claude Code workflows, justify automation investments, and ensure their toolkit evolves based on real-world performance data.

主な機能

Data-driven insight extraction for toolkit optimization
Automated usage data and metrics collection
ROI calculation based on time-savings and efficiency gains
Quantitative assessment of skill quality and success rates
0 GitHub stars
Pattern recognition and trend analysis over time

ユースケース

Measuring the tangible ROI of newly implemented AI workflows
Generating performance reports to justify AI tool adoption to stakeholders
Identifying underperforming or underutilized skills within a repository

概要

主な機能

Data-driven insight extraction for toolkit optimization
Automated usage data and metrics collection
ROI calculation based on time-savings and efficiency gains
Quantitative assessment of skill quality and success rates
0 GitHub stars
Pattern recognition and trend analysis over time

ユースケース

Measuring the tangible ROI of newly implemented AI workflows
Generating performance reports to justify AI tool adoption to stakeholders
Identifying underperforming or underutilized skills within a repository