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:
checklists/context-efficiency.json- Context window usagechecklists/startup-performance.json- Plugin initializationchecklists/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 measurementsbottlenecks: Identified performance issuesoptimizations: Recommended improvements