# Perf And Memory

> 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.

- Skill: `ashermahonin/perf-and-memory` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add ashermahonin/perf-and-memory`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ashermahonin/perf-and-memory/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: ashermahonin (https://skillmd.com/u/ashermahonin)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/ashermahonin/perf-and-memory

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# Performance and Memory

## Purpose

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

## Change context

- 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.

## Change method

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.

## Engineering constraints

- Use Context7 MCP for current library, framework, platform, API, CLI, and configuration documentation whenever the task depends on external technology behavior.

## Evidence

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

## Ready when

- 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.