# Performance Engineering > Use when latency, throughput, CPU, memory, allocation, I/O, scale, or cost is explicit. Requires measurement; exclude unknown-cause regressions and speculative tuning. - Skill: `devguyrash/performance-engineering` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds@latest add devguyrash/performance-engineering` - Raw SKILL.md: https://api.skillmd.com/api/skills/devguyrash/performance-engineering/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DevGuyRash (https://skillmd.com/u/devguyrash) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/devguyrash/performance-engineering --- # Performance Engineering Make performance work falsifiable. Define what should improve, measure a representative baseline, localize the limiting resource, and compare the result without silently spending correctness or another resource. ## Define the performance contract Before changing implementation, establish: - the metric and unit, such as p99 latency, throughput, peak resident memory, allocations, artifact size, energy, or cost per operation; - the target or decision threshold; - the workload, input distribution, concurrency, scale, and steady-state or cold-start conditions that matter; - offered, admitted, completed, rejected, timed-out, and failed work when load or queues can change how much useful work is measured; - correctness, security, reliability, readability, and resource guardrails; - the environment and repository-native command used for comparison. If the request is merely “make it faster,” derive a decision-relevant criterion from available product or operational evidence. Do not invent a target whose tradeoffs belong to the user or system owner. ## Establish a trustworthy baseline Run the unchanged system under the chosen workload. Preserve raw results and enough environment metadata to reproduce the comparison. Use repeated or interleaved measurements when noise could change the decision. Read [measurement-validity.md](/references/measurement-validity.md) before designing a new benchmark, comparing noisy results, measuring JIT or managed runtimes, or translating a microbenchmark result into a system-level claim. Read [complexity-and-data-structures.md](/references/complexity-and-data-structures.md) when the requested result depends on input growth, repeated passes, allocation shape, or choosing a data structure or algorithm. Read [load-parallelism-and-resources.md](/references/load-parallelism-and-resources.md) when the result involves queues, tail latency, async or parallel execution, worker counts, contention, topology, or a CPU, memory, storage, filesystem, or network ceiling. Read [optimization-mechanics.md](/references/optimization-mechanics.md) only after evidence localizes cost and the work needs language-neutral candidate mechanisms. ## Find the limiting path Profile the representative workload with the least-distorting tool available. Select CPU, allocation, heap, I/O, lock, scheduler, trace, or system counters based on the metric. Attribute cost to a path and input before optimizing it. Algorithmic complexity helps predict scaling, but an asymptotic improvement is not runtime evidence. Constant factors, cache behavior, allocation, vectorization, contention, and realistic input sizes can reverse an intuition. ## Run one supported experiment 1. State the bottleneck hypothesis and predicted metric effect. 2. Choose the smallest change that tests it without violating guardrails. 3. Keep the benchmark and environment comparable to the baseline. 4. Verify functional correctness and equivalent useful work before accepting the measurement. 5. Compare magnitude, variability, queue or concurrency state, limiting-resource utilization, and any shifted resource cost. 6. Keep the change only when the evidence justifies its complexity and tradeoffs. Re-profile after a meaningful win: the bottleneck may move. Stop when the target is met, the next opportunity is outside scope, uncertainty exceeds the apparent gain, or further cost is not justified. ## Match evidence to the claim - Use end-to-end or workload benchmarks for user-visible latency and throughput. - Use microbenchmarks to isolate a known hot operation, not to stand in for the whole product. - Report latency distributions for tail-sensitive systems; an average can hide the requested regression. - Include memory, I/O, cost, or energy deltas when the optimization can exchange one resource for another. - Test scaling at relevant sizes when the claim concerns complexity or capacity. ## Compose with neighboring skills - When a slowdown's cause is unknown, systematic debugging leads until evidence identifies the cause. Use this skill to validate the proposed performance fix. - Let refactoring own a local behavior-neutral restructuring when performance evidence has already selected it; this skill still owns the before/after claim. - Let language, runtime, database, and platform skills select appropriate tools and interpret domain-specific profiles. - Let test-driven development protect intentional behavior changes; benchmarks do not replace correctness tests. ## Completion evidence Report the criterion, workload and load model, environment, useful-work counts, baseline, profile evidence, limiting resource, change, after measurements, variability, queue/concurrency state when relevant, and guardrail results. State whether the target was met and name limitations that prevent a broader claim. Do not promise improvement from Big-O analysis alone, require one statistical test or percentage threshold for every workload, optimize dead code, discard negative results, or hide memory, security, reliability, and maintenance costs.