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swiftui-performance-audit

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by openai · part of openai/plugins

Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory…

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🧩 One of 7 skills in the openai/plugins package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.


name: swiftui-performance-audit description: Audit SwiftUI runtime performance from code first. Use when diagnosing slow rendering, janky scrolling, expensive updates, or profiling needs.

SwiftUI Performance Audit

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
  • In addition to the references above, use web search to consult current Apple Developer documentation when Instruments workflows or SwiftUI performance guidance may have changed.