How does this skill measure token consumption?

It uses automated scripts to estimate the prompt size of skills and agents against established token limits to ensure cost-effective AI interactions.

Can this skill help reduce latency in Claude Code?

Yes, by identifying slow startup files and excessive file access patterns, you can optimize your plugin to respond faster and use fewer resources.

What is considered a good efficiency score for a plugin?

A score closer to 100 is ideal. The tool identifies specific bottlenecks such as excessive file reads or large prompt sizes if your score falls below targets.

Does it support lazy loading validation?

Yes, it specifically includes scripts and checklists to validate that plugins are loading resources efficiently only when needed.

PopKit Performance Assessment

Name: PopKit Performance Assessment
Author: jrc1883

byjrc1883

Analíticas y Monitorización

Evaluates and optimizes Claude Code plugin performance using automated metrics for token consumption and context efficiency.

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The PopKit Performance Assessment skill provides a comprehensive framework for auditing the efficiency of Claude Code plugins. It automates the collection of critical performance data, including context window usage, token consumption, and lazy loading validation, helping developers ensure their tools remain lightweight and responsive. By utilizing standardized checklists and performance targets, it identifies bottlenecks in file access patterns and plugin initialization, ultimately delivering an efficiency score and actionable optimization recommendations to improve the developer experience.

Características Principales

Comprehensive efficiency scoring (0-100) and bottleneck identification
Validation of lazy loading patterns for plugin initialization
Automated context window usage analysis and token estimation
Reproducible reporting for plugin optimization and auditing
Standardized performance checklists for file access and startup
0 GitHub stars

Casos de Uso

Identifying inefficient file-read patterns during automated workflows
Benchmarking plugin startup times against standardized performance targets
Auditing a complex plugin to reduce token costs and context bloat

Acerca de

Características Principales

Comprehensive efficiency scoring (0-100) and bottleneck identification
Validation of lazy loading patterns for plugin initialization
Automated context window usage analysis and token estimation
Reproducible reporting for plugin optimization and auditing
Standardized performance checklists for file access and startup
0 GitHub stars

Casos de Uso

Identifying inefficient file-read patterns during automated workflows
Benchmarking plugin startup times against standardized performance targets
Auditing a complex plugin to reduce token costs and context bloat