principle-performance

What problem does it solve?

This Skill prevents performance regressions by teaching how to choose the right design decisions (algorithms, data structures, access patterns) before you write code or change implementations.

Core Features & Use Cases

• Latency vs. Throughput trade-offs: Decide the actual optimization objective (including tail latency) instead of improving the wrong metric.
• Profile-before-optimize discipline: Ensure changes target real bottlenecks with measurable before/after validation.
• Hot-path performance thinking: Catch common causes of slowdowns such as O(n²) patterns, excessive allocations/GC pressure, poor data locality, and N+1 database queries.

Quick Start

Ask an AI to review a suspected hot-path endpoint design and point out likely latency/throughput issues, N+1 query risks, and allocation-heavy patterns to prioritize what to measure and change first.