performance-optimization
Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.
Performance Optimization
Overview
Measure before optimizing. Performance work without measurement is guessing — and guessing leads to premature optimization that adds complexity without improving what matters. Profile first, identify the actual bottleneck, fix it, measure again. Optimize only what measurements prove matters.
When to Use
- Performance requirements exist in the spec (load time budgets, response time SLAs)
- Users or monitoring report slow behavior
- Core Web Vitals scores are below thresholds
- You suspect a change introduced a regression
- Building features that handle large datasets or high traffic
When NOT to use: Don't optimize before you have evidence of a problem. Premature optimization adds complexity that costs more than the performance it gains.
Core Web Vitals Targets
| Metric | Good | Needs Improvement | Poor |
|---|---|---|---|
| LCP (Largest Contentful Paint) | ≤ 2.5s | ≤ 4.0s | > 4.0s |
| INP (Interaction to Next Paint) | ≤ 200ms | ≤ 500ms | > 500ms |
| CLS (Cumulative Layout Shift) | ≤ 0.1 | ≤ 0.25 | > 0.25 |
The Optimization Workflow
1. MEASURE → Establish baseline with real data
2. IDENTIFY → Find the actual bottleneck (not assumed)
3. FIX → Address the specific bottleneck
4. VERIFY → Measure again, confirm improvement
5. GUARD → Add monitoring or tests to prevent rSubscribers only
The full skill, its 1 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | addyosmani/agent-skills |
|---|---|
| License | MIT |
| Risk label | critical ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md |
| Added | 2026-07-01 |
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