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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Repository: web-infra-dev/rsbuild
Letzte Quellaktivität: 22. Juni 2026 um 02:48
Erkannte Sprache von SKILL.md: Englisch
Sterne: 3.364
Forks: 273

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

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

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

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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