When to Use
- Profile services under load — CPU, heap, goroutines, GC, lock contention
- Trace requests end-to-end — identify critical path and fan-out
- Load test — baseline, soak, stress, spike; interpret saturation and errors
- Define budgets — p50/p95/p99 targets, error budget, per-dependency ceilings
- Tune databases, caches, queues, and hot code paths with measured before/after
- Detect regressions — benchmark suites, CI gates, release comparisons
- Model capacity — headroom, scaling triggers, cost vs latency trade-offs
- Report findings — reproducible steps, flame graphs, ranked recommendations
When NOT to Use
- Build or fix Jenkins/GitHub Actions pipelines →
devops - Canary/blue-green rollout plans →
deployment-strategist - Star schema, dbt layers, warehouse ELT design →
data-warehouse-engineer,analytics-data-engineer - Implement React components or a11y →
senior-frontend-software-engineer - General feature delivery without perf focus →
senior-software-engineer - DC power/cooling/rack utilization programs →
data-center-compute-supply-efficiency - LLM token benchmarks and compression research →
research-engineer-scientist-tokens - Enterprise architecture NFR sign-off only →
senior-system-architecture
Related skills
| Need | Skill |
|---|---|
| Implement fixes in application code | senior-software-engineer |
| SLO dashboards, alerting, on-call for deploy | devops |
| Core Web Vitals in product UI code | senior-frontend-software-engineer |
| Warehouse SQL and partition design | data-warehouse-engineer |
| Cross-service latency budgets in ADRs | senior-system-architecture |
| Data pipeline SLA and batch windows | data-system-ops-lead |
| GPU/cluster capacity for training | data-center-compute-supply-efficiency |
| Token/cost efficiency experiments | research-engineer-scientist-tokens |
Core Workflows
1. Profiling and diagnostics
CPU, memory, I/O, contention.
See references/profiling_diagnostics.md.
2. Load and capacity testing
Scenarios, harness, interpretation.
See references/load_capacity_testing.md.
3. Latency, throughput, and SLOs
Percentiles, budgets, error budget.
See references/latency_slo_budgets.md.
4. Database and data-path tuning
Queries, indexes, caches.
See references/database_query_performance.md.
5. Frontend and client runtime
CWV, network, rendering.
See references/frontend_runtime_performance.md.
6. Regression detection and reporting
Baselines, CI, executive summary.
See references/regression_ci_reporting.md.
Outputs
- Perf investigation brief — symptom, scope, hypothesis, reproduction
- Trace + profile pack — flame graph, span waterfall, top offenders
- Load test plan and results — scenario matrix, graphs, bottlenecks
- Optimization backlog — ranked by impact × effort with measurements
- SLO proposal — targets, measurement points, alert thresholds
- Regression report — build-over-build comparison with root cause
Principles
- Measure first — no optimization without baseline and success metric
- One variable — isolate changes; document environment (hardware, data size, version)
- User-centric metrics — tail latency and error rate over averages
- Saturation-aware — distinguish queueing from inefficient work
- Reproducible — scripts, seeds, and configs checked in or attached to report