pop-assessment-performance

jrc1883's avatarfrom jrc1883

Evaluates PopKit efficiency using concrete metrics for context usage, token consumption, and lazy loading validation

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[Evaluation and Benchmarks]

When & Why to Use This Skill

This Claude skill provides a comprehensive performance assessment framework for PopKit plugins, focusing on context efficiency, token consumption, and system resource optimization. It enables developers to audit and improve AI agent performance through measurable metrics, automated analysis scripts, and standardized performance targets.

Use Cases

  • Token Usage Optimization: Analyze and reduce token consumption to lower API costs and improve response latency for AI agents.
  • Context Window Management: Evaluate context usage to ensure critical information is prioritized and prevent model performance degradation due to context overflow.
  • Startup & Lazy Loading Validation: Audit plugin initialization patterns to minimize resource overhead and ensure efficient file access during agent activation.
  • Automated Performance Reporting: Generate detailed JSON reports with efficiency scores and actionable optimization recommendations for technical audits or CI/CD integration.
namepop-assessment-performance
description"Evaluates PopKit efficiency using concrete metrics for context usage, token consumption, and lazy loading validation"
contextfork
version1.0.0

Performance Assessment Skill

Purpose

Provides concrete, reproducible performance assessment for PopKit plugins using:

  • Measurable efficiency metrics
  • Automated context analysis
  • Token consumption estimation
  • Lazy loading validation

How to Use

Step 1: Run Automated Metrics Collection

python skills/pop-assessment-performance/scripts/measure_context.py packages/plugin/
python skills/pop-assessment-performance/scripts/analyze_loading.py packages/plugin/
python skills/pop-assessment-performance/scripts/calculate_efficiency.py packages/plugin/

Step 2: Apply Performance Checklists

Read and apply checklists in order:

  1. checklists/context-efficiency.json - Context window usage
  2. checklists/startup-performance.json - Plugin initialization
  3. checklists/file-access-patterns.json - Read/write efficiency

Step 3: Generate Report

Combine automated metrics with checklist results for final performance report.

Standards Reference

Standard File Key Checks
Context Efficiency standards/context-efficiency.md CE-001 through CE-008
Startup Performance standards/startup-performance.md SP-001 through SP-006
File Access standards/file-access.md FA-001 through FA-008
Token Consumption standards/token-consumption.md TC-001 through TC-006

Performance Targets

Metric Target Warning Critical
Skill Prompt Size <2000 tokens 2000-4000 >4000
Agent Prompt Size <5000 tokens 5000-8000 >8000
Tier-1 Agent Count <=15 16-20 >20
File Reads/Operation <5 5-10 >10
Startup Files <10 10-20 >20

Output

Returns JSON with:

  • efficiency_score: 0-100 (higher = better)
  • metrics: Collected performance measurements
  • bottlenecks: Identified performance issues
  • optimizations: Recommended improvements