performance

1# Performance optimization
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3Evidence-led performance optimization using real-user signals for prioritization and browser traces for diagnosis. Focuses on loading speed, runtime responsiveness, and resource delivery.
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5## How it works
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71. If a page can run, read [the measurement workflow](references/MEASUREMENT.md) and establish a field-plus-lab baseline before editing.
82. Prioritize poor real-user Core Web Vitals. Use a DevTools performance trace and its focused insights to find the cause.
93. Inspect and change only the code or assets connected to measured bottlenecks.
104. Re-run equivalent lab measurements and report before/after values, conditions, and uncertainty. Field verification remains pending until enough new user data arrives.
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12When no runnable page exists, perform static inspection but call findings **hypotheses**, not measured regressions. Include the command or browser workflow that can verify each high-impact hypothesis.
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14Prefer a browser tool that records a performance trace and exposes focused insights. With Chrome DevTools MCP, use `performance_start_trace` and `performance_analyze_insight`; do not route performance through `lighthouse_audit`, which covers non-performance Lighthouse categories.
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16## Starting performance budget
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