go-benchmark
SolidGo performance benchmarking and pprof profiling specialist. ALWAYS use when writing benchmark functions (testing.B), generating or reading pprof profiles, interpreting flame graphs, finding memory allocation hotspots, comparing implementations with benchstat, or measuring ns/op / B/op / allocs/op. In Go code contexts, also trigger when the user says "it's slow", "too many allocations", "find the bottleneck", or "profile this Go code".
Install
Quality Score: 85/100
Skill Content
Details
- Author
- johnqtcg
- Repository
- johnqtcg/awesome-skills
- Created
- 4 months ago
- Last Updated
- yesterday
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
golang-benchmark
Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.
golang-performance
Golang 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).
go-performance
Performance: Use when latency, throughput, allocation, contention, memory, capacity, or workload budgets need a decision; when a design introduces workload amplification such as per-item I/O or serialization, growing scans, fan-out, batching, retention, or contention; or when changed hot paths and benchmark/profile evidence need review. Own measurable performance policy and conformance; Skip when correctness, reliability, or DB/cache policy is primary.