golang-performance
SolidGolang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (see golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill).
Install
Quality Score: 78/100
Skill Content
Details
- Author
- guynhsichngeodiec
- Repository
- guynhsichngeodiec/cc-skills-golang
- Created
- 3 months ago
- Last Updated
- yesterday
- Language
- Go
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
perf-profiling
Systematic performance profiling and optimization. Use when performance issues are reported or suspected. Measure first, optimize second. Applies Pareto principle — find the 20% of code causing 80% of slowness, fix that, not the rest.
principle-performance
Performance engineering principles — latency vs throughput, profile-before-optimize discipline, Big-O instincts for common patterns, allocation and GC pressure, data locality and cache-friendliness, N+1 queries on a list endpoint, cache-aside and write-through invalidation, TTL sizing for hot-path endpoints, stampede prevention with single-flight. Auto-load when reviewing hot-path code, choosing data structures, designing batch or streaming pipelines, hunting allocations or GC pauses, weighing latency trade-offs, sizing TTL and picking cache invalidation, preventing cache stampede or thundering herd with single-flight on an expired hot key, detecting N+1 queries on list endpoint calls, or evaluating scalability.
performance-profiler
Systematically finds and fixes performance bottlenecks by measuring first, profiling hot paths, reducing algorithmic and I/O cost, and verifying gains with before/after benchmarks. Use this skill when code, an endpoint, a page, a query, or a job is "slow", "laggy", "timing out", "using too much CPU/memory", or "doesn't scale"; when asked to optimize, speed up, profile, or benchmark something; when chasing high latency / low throughput, p99 spikes, memory leaks, N+1 queries, or excessive allocations; or before/after a perf-sensitive change to prove it actually helped. Covers Python, Node/JS, Go, Java, SQL, and HTTP/web frontends.