Lists multiple specific concrete actions and domains: allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, hot-path optimization, and performance code review. The 'if X bottleneck, then apply Y' framing adds methodological specificity.

Clearly answers both 'what' (Golang performance optimization patterns covering allocation reduction, CPU efficiency, etc.) and 'when' (explicit 'Use when profiling or benchmarks have identified a bottleneck' and 'when performing performance code review'). Also includes explicit 'Not for' exclusions referencing other skills, which strengthens the 'when' guidance.

Includes strong natural keywords users would say: 'performance optimization', 'profiling', 'benchmarks', 'bottleneck', 'allocation reduction', 'GC tuning', 'caching', 'hot-path', 'performance code review'. These cover a wide range of terms a Go developer would naturally use when seeking optimization help.

Highly distinctive with explicit boundary-setting via 'Not for measurement methodology (see golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill)'. This directly addresses potential overlap with related skills and creates clear delineation.

The content is lean and efficient. It assumes Claude knows Go, avoids explaining basic concepts, and every section earns its place — the decision tree, common mistakes table, and methodology steps are all high-density information. No unnecessary padding or concept explanations.

The iterative methodology provides concrete commands (benchstat, go test -bench flags), and the decision tree maps signals to actions. However, the actual optimization patterns are delegated to reference files rather than shown inline — the main skill lacks executable code examples demonstrating specific optimizations. The common mistakes table provides actionable fixes but they're brief directives rather than copy-paste ready code.

The iterative optimization cycle is clearly sequenced with 7 explicit steps including baseline measurement, single-change discipline, and statistical comparison via benchstat. The 'Rule Out External Bottlenecks First' section acts as a validation checkpoint before entering the optimization loop. The feedback loop (measure → change one thing → re-measure → repeat) is explicit and well-structured.

Excellent structure: the main skill provides a concise overview with decision tree, methodology, and common mistakes, then clearly signals one-level-deep references to 6 detailed deep-dive files. Cross-references to other skills are well-organized and clearly labeled. No deeply nested references or monolithic content blocks.