rsbuild-performance-profiling

Use when profiling, analyzing, reporting, or optimizing Rsbuild performance, including build, dev server, rebuild, static asset serving, memory usage, and CPU profile analysis.

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Dépôt: web-infra-dev/rsbuild
Dernière activité de la source: 22 juin 2026 à 02:48
Langue détectée de SKILL.md: anglais
Étoiles: 3 364
Forks: 273

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.

npx skills add https://github.com/web-infra-dev/rsbuild --skill rsbuild-performance-profiling

La commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.

Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

SKILL.md

Instructions source · Aperçu en lecture seule

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Tout voir dans ce dépôt

Rsbuild Performance Profiling

Workflow

Define the target scenario before collecting data:

operation: build, dev startup, rebuild, request serving, memory usage, or another explicit flow
benchmark project or fixture
metric: wall time, CPU time, self time, allocations, RSS, heap
success threshold, if the user provided one

Record the baseline first. Keep runtime, package manager, dependencies, environment variables, command flags, and input project unchanged for comparisons.

Save raw artifacts when available, such as CPU profiles, heap snapshots, logs, timing output, and benchmark commands. Do not rely only on subjective observations.

Separate costs by ownership:

Rsbuild code paths
Rspack or loader/plugin internals
third-party dependencies
user project code or benchmark fixture code

Rank findings by measured impact. Prefer self time for direct optimization targets and total time for understanding call-chain impact.

If implementing an optimization, keep behavior unchanged unless the user explicitly asked for a behavior change. Prefer small, measurable changes over broad refactors.

Re-run the same benchmark after changes. Report before/after numbers, absolute delta, percentage delta, and any variance or confidence limits you can observe.

Analysis Rules

Treat the user's supplied profiles, reports, and benchmark numbers as the source of truth for that task.

Do not claim a performance win without comparable before/after measurements.

Do not optimize generated output, dependencies, or benchmark fixture code unless the user asked for that scope.

For CPU profiles, call out both hot functions and why they are on the hot path.

For memory tasks, distinguish peak usage from stable usage after warmup or repeated rebuilds.

Output

Return a concise report with:

benchmark command or data source

top findings ordered by impact

Rsbuild-owned optimization opportunities

changes made, if any

validation results and remaining risk