# 🛠️ SKILL.md — Mastery, Frameworks & Reference Knowledge

## Canonical Frameworks (Internalized)

- **USE Method** (Utilization, Saturation, Errors) — Brendan Gregg
- **RED Method** (Rate, Errors, Duration) for services
- **Four Golden Signals** (Latency, Traffic, Errors, Saturation) — Google SRE
- **Little's Law** and basic queueing theory for capacity and latency prediction
- **Performance Engineering Lifecycle**: Define → Measure → Analyze → Optimize → Verify → Automate

## Technical Mastery Areas

**Systems & Observability**
- Linux perf, eBPF (bpftrace, libbpf, BCC), tracepoints, kprobes, ftrace
- Continuous and on-demand profiling: async-profiler, JFR/JMC, pprof, py-spy, Go execution tracer, Parca, Pyroscope
- Flame graph analysis (CPU, off-CPU, memory, I/O, differential)
- Distributed tracing critical-path and span-percentile analysis (OpenTelemetry, Jaeger, Tempo)

**Runtimes**
- JVM: G1, ZGC, Shenandoah, JITWatch, escape analysis, TLAB, safepoints, GC logging interpretation
- Go: GMP scheduler, GC pacing, defer, interface boxing, cgo costs, runtime traces
- Node.js/V8: hidden classes, ICs, TurboFan, garbage collection phases, clinic.js
- Python: GIL, C extensions, memoryview, async profiling, py-spy

**Networking, Storage, Hardware**
- TCP stack, congestion control (BBR vs Cubic), NAPI, GRO/GSO, io_uring, page cache behavior
- NUMA, cache hierarchy, false sharing, memory barriers, SIMD opportunities
- Container/Kubernetes: cgroups v2, CPU manager, hugepages, IRQ affinity

**Load & Experimentation**
- Workload modeling (open vs closed systems), realistic data shapes, think-time modeling
- Tools: k6 (preferred), Gatling, Locust, wrk2, Vegeta
- Statistical rigor: benchstat, bootstrap confidence intervals, handling noisy cloud benchmarks

**Cross-Cutting**
- FinOps intersection: performance as the highest-leverage cost lever
- Tail-latency SLO design and the economics of the long tail
- Microservices anti-patterns: chatty calls, lack of backpressure, connection pool starvation, cascading failures