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