Performance engineering

Skill jacob-balslev/skills/skills/quality-assurance/performance-engineering

Public Agent Skills library exported from skill-graph. Install: npx skills add jacob-balslev/skills

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npx -y skills add jacob-balslev/skills --skill performance-engineering

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Use when measuring, diagnosing, budgeting, or improving performance: latency, throughput, Core Web Vitals, database queries, caching, bundle size, concurrency, resource use, and regression prevention. Do NOT use for telemetry schema design alone (use `observability-modeling`), error capture setup (use `error-tracking`), or premature micro-optimization without a measured bottleneck.

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SKILL.md

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Performance Engineering

Concept of the skill

Measuring, diagnosing, budgeting, and improving performance — latency, throughput, Core Web Vitals, database queries, caching, bundle size, concurrency, resource use, and regression prevention.

Coverage

Measure and improve performance across frontend, backend, database, jobs, APIs, and tooling. Covers bottleneck analysis, performance budgets, Core Web Vitals, query plans, N+1 detection, caching, batching, concurrency, bundle size, resource use, regression checks, and tradeoffs.

Philosophy of the skill

Measure first. Performance work without measurement is guessing, and guessing usually optimizes the easiest code rather than the bottleneck. The correct target is the user-visible or business-critical bottleneck with evidence.

Performance is also a contract. If speed matters, define budgets and regression checks before the system silently decays.

Method

Define the performance goal and user/business impact.
Collect baseline measurements under realistic conditions.
Identify the bottleneck: network, server, database, rendering, bundle, CPU, memory, lock contention, or third party.
Choose the smallest intervention likely to move the bottleneck.
Verify improvement with the same measurement method.
Add a budget, alert, or regression test for the fixed surface.
Record tradeoffs such as freshness, complexity, cost, or cache invalidation risk.

Evals

This skill ships a comprehension-eval artifact at examples/evals/performance-engineering.json. The checklist below is the authoring gate for performance decisions; the eval file is the grader surface.

Verification

Baseline and post-change measurements use the same method
The optimized target is the measured bottleneck
User-visible or business impact is stated
Cache changes include invalidation and staleness rules
Database fixes include query-plan or index evidence when relevant
Frontend fixes include bundle or Web Vitals evidence when relevant
A regression guard exists for important performance surfaces

Do NOT Use When

Use instead	When
`observability-modeling`	You need to design telemetry schema and diagnostic signals.
`error-tracking`	You need error capture, redaction, source maps, or issue triage.
`testing-strategy`	You need general correctness test planning.
`refactor`	You are restructuring code without a measured performance goal.