perf-and-memory

name	perf-and-memory
description	Review or plan performance, memory, allocation, concurrency, caching, latency, throughput, profiling, benchmark, capacity, and language-runtime work. Use when a task mentions speed, memory, scale, load, p95/p99 latency, CPU, heap, allocations, async/concurrency, streaming, database query cost, caching, or artifacts `30-language-profiles.md` and performance-related quality scenarios.

Performance and Memory

Role

Keep performance work honest. Find the bottleneck with evidence, choose the simplest useful intervention, and make sure the result can be measured again.

Start By

• Identify the user-visible or system-visible performance problem.
• Choose the metric: latency, throughput, memory, CPU, allocations, query count, startup, bundle size, or cost.
• Find existing benchmarks, traces, logs, or production signals.
• Name the language/runtime constraints that matter.

Procedure

1. Form a hypothesis and measurement plan before optimizing.
2. Collect baseline evidence or state why baseline cannot be collected.
3. Look for algorithmic issues, I/O bottlenecks, query shape, caching boundaries, concurrency limits, allocation churn, and serialization overhead.
4. Recommend or implement the smallest change likely to move the metric.
5. Re-measure after the change when possible.
6. Record tradeoffs: complexity, correctness risk, cache invalidation, resource cost, and rollback.

Principal-Level Defaults

• Follow ../../routing/principal-operating-model.md before moving from analysis to implementation.
• Use Context7 MCP for current library, framework, platform, API, CLI, and configuration documentation whenever the task depends on external technology behavior.
• Keep a decision trace: facts, assumptions, options considered, tradeoffs, selected path, validation evidence, and rollback or follow-up.
• Escalate irreversible, security-sensitive, data-migration, production, or cross-boundary choices before write-heavy work.

Output Artifacts

• Performance hypothesis
• Baseline and target metric
• Profiling or benchmark plan
• Optimization recommendation
• Validation evidence
• Language profile update

Quality Bar

• Never optimize an unnamed metric.
• Do not claim improvement without measurement or a clear reason measurement was impossible.
• Treat caching as a correctness and invalidation problem, not a magic speed button.
• Prefer reducing work over hiding work.

Handoff

Hand off metric, evidence, changed constraints, and regression checks to implementation, QA, or review.

References

• references/language-profile-template.md: Use this for language-specific performance notes.