Guide Swiftui Performance Audit

Prisma-Labs-Dev/apple-skills/skills/guide-swiftui-performance-audit

作者 Prisma-Labs-Dev498626005e65783ebe8570a103b2335cf397768cMIT收錄於 2026年10月9日更新於 2026年10月9日

Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.

AI 產生的概覽

透過程式碼審查稽核 SwiftUI 執行效能,並引導使用者自行以 Instruments 進行效能分析。

功能
這份指南技能會診斷 SwiftUI 效能問題,例如繪製緩慢、捲動卡頓、CPU 或記憶體用量過高、卡住,以及檢視更新過於頻繁。它先以程式碼審查為主,使用程式碼異味清單進行分析,當程式碼審查無法得出結論時,再索取效能分析證據。最後產出依影響程度排序的稽核結果,包含可能根本原因、修正建議,以及前後對照的指標表。
適用情境
當 SwiftUI 應用程式出現繪製緩慢、掉格、CPU 或記憶體壓力過高、卡住,或檢視更新範圍異常廣泛時使用。也適合需要結構化指引來蒐集 Instruments 追蹤資料,或需要一份書面稽核報告的情況。
執行需求
不需要指令碼,只有說明與參考文件。需要目標檢視或功能程式碼、症狀與重現細節,以及選用的使用者提供 Instruments 追蹤資料或截圖。效能分析由使用者在 Xcode Instruments 中自行執行。

Guide Skill — This is an expert workflow/pattern guide, not API reference documentation. Originally from Dimillian/Skills by Thomas Ricouard. MIT License.

SwiftUI Performance Audit

Quick start

Use this skill to diagnose SwiftUI performance issues from code first, then request profiling evidence when code review alone cannot explain the symptoms.

Workflow

  1. Classify the symptom: slow rendering, janky scrolling, high CPU, memory growth, hangs, or excessive view updates.
  2. If code is available, start with a code-first review using references/code-smells.md.
  3. If code is not available, ask for the smallest useful slice: target view, data flow, reproduction steps, and deployment target.
  4. If code review is inconclusive or runtime evidence is required, guide the user through profiling with references/profiling-intake.md.
  5. Summarize likely causes, evidence, remediation, and validation steps using references/report-template.md.

1. Intake

Collect:

  • Target view or feature code.
  • Symptoms and exact reproduction steps.
  • Data flow: @State, @Binding, environment dependencies, and observable models.
  • Whether the issue shows up on device or simulator, and whether it was observed in Debug or Release.

Ask the user to classify the issue if possible:

  • CPU spike or battery drain
  • Janky scrolling or dropped frames
  • High memory or image pressure
  • Hangs or unresponsive interactions
  • Excessive or unexpectedly broad view updates

For the full profiling intake checklist, read references/profiling-intake.md.

2. Code-First Review

Focus on:

  • Invalidation storms from broad observation or environment reads.
  • Unstable identity in lists and ForEach.
  • Heavy derived work in body or view builders.
  • Layout thrash from complex hierarchies, GeometryReader, or preference chains.
  • Large image decode or resize work on the main thread.
  • Animation or transition work applied too broadly.

Use references/code-smells.md for the detailed smell catalog and fix guidance.

Provide:

  • Likely root causes with code references.
  • Suggested fixes and refactors.
  • If needed, a minimal repro or instrumentation suggestion.

3. Guide the User to Profile

If code review does not explain the issue, ask for runtime evidence:

  • A trace export or screenshots of the SwiftUI timeline and Time Profiler call tree.
  • Device/OS/build configuration.
  • The exact interaction being profiled.
  • Before/after metrics if the user is comparing a change.

Use references/profiling-intake.md for the exact checklist and collection steps.

4. Analyze and Diagnose

  • Map the evidence to the most likely category: invalidation, identity churn, layout thrash, main-thread work, image cost, or animation cost.
  • Prioritize problems by impact, not by how easy they are to explain.
  • Distinguish code-level suspicion from trace-backed evidence.
  • Call out when profiling is still insufficient and what additional evidence would reduce uncertainty.

5. Remediate

Apply targeted fixes:

  • Narrow state scope and reduce broad observation fan-out.
  • Stabilize identities for ForEach and lists.
  • Move heavy work out of body into derived state updated from inputs, model-layer precomputation, memoized helpers, or background preprocessing. Use @State only for view-owned state, not as an ad hoc cache for arbitrary computation.
  • Use equatable() only when equality is cheaper than recomputing the subtree and the inputs are truly value-semantic.
  • Downsample images before rendering.
  • Reduce layout complexity or use fixed sizing where possible.

Use references/code-smells.md for examples, Observation-specific fan-out guidance, and remediation patterns.

6. Verify

Ask the user to re-run the same capture and compare with baseline metrics. Summarize the delta (CPU, frame drops, memory peak) if provided.

Outputs

Provide:

  • A short metrics table (before/after if available).
  • Top issues (ordered by impact).
  • Proposed fixes with estimated effort.

Use references/report-template.md when formatting the final audit.

References

  • Profiling intake and collection checklist: references/profiling-intake.md
  • Common code smells and remediation patterns: references/code-smells.md
  • Audit output template: references/report-template.md
  • Add Apple documentation and WWDC resources under references/ as they are supplied by the user.
  • Optimizing SwiftUI performance with Instruments: references/optimizing-swiftui-performance-instruments.md
  • Understanding and improving SwiftUI performance: references/understanding-improving-swiftui-performance.md
  • Understanding hangs in your app: references/understanding-hangs-in-your-app.md
  • Demystify SwiftUI performance (WWDC23): references/demystify-swiftui-performance-wwdc23.md

來源與署名

來源:Prisma-Labs-Dev/apple-skills位於skills/guide-swiftui-performance-audit提交4986260

授權條款: MIT

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

檢舉或申請下架