Co-Pilot
Updated a month ago

swiftui-performance-audit

DDimillian
1.5k
Dimillian/Skills/swiftui-performance-audit
82
Agent Score

💡 Summary

A diagnostic skill that audits SwiftUI app performance through code review and guides users to profile with Instruments for issues like slow rendering and high memory usage.

🎯 Target Audience

SwiftUI App DevelopersMobile Performance EngineersiOS Tech LeadsFreelance iOS Developers

🤖 AI Roast:This skill is like a performance mechanic who can diagnose your car's engine from a sound clip, but you still have to get your hands dirty with the actual tools.

Security AnalysisLow Risk

Read-only advisory skill with no execution. Risk: Sensitive data in user-provided code/traces. Mitigation: Sanitize inputs and implement validation in the hosting agent.


name: swiftui-performance-audit description: 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.

SwiftUI Performance Audit

Overview

Audit SwiftUI view performance end-to-end, from instrumentation and baselining to root-cause analysis and concrete remediation steps.

Workflow Decision Tree

  • If the user provides code, start with "Code-First Review."
  • If the user only describes symptoms, ask for minimal code/context, then do "Code-First Review."
  • If code review is inconclusive, go to "Guide the User to Profile" and ask for a trace or screenshots.

1. Code-First Review

Collect:

  • Target view/feature code.
  • Data flow: state, environment, observable models.
  • Symptoms and reproduction steps.

Focus on:

  • View invalidation storms from broad state changes.
  • Unstable identity in lists (id churn, UUID() per render).
  • Heavy work in body (formatting, sorting, image decoding).
  • Layout thrash (deep stacks, GeometryReader, preference chains).
  • Large images without downsampling or resizing.
  • Over-animated hierarchies (implicit animations on large trees).

Provide:

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

2. Guide the User to Profile

Explain how to collect data with Instruments:

  • Use the SwiftUI template in Instruments (Release build).
  • Reproduce the exact interaction (scroll, navigation, animation).
  • Capture SwiftUI timeline and Time Profiler.
  • Export or screenshot the relevant lanes and the call tree.

Ask for:

  • Trace export or screenshots of SwiftUI lanes + Time Profiler call tree.
  • Device/OS/build configuration.

3. Analyze and Diagnose

Prioritize likely SwiftUI culprits:

  • View invalidation storms from broad state changes.
  • Unstable identity in lists (id churn, UUID() per render).
  • Heavy work in body (formatting, sorting, image decoding).
  • Layout thrash (deep stacks, GeometryReader, preference chains).
  • Large images without downsampling or resizing.
  • Over-animated hierarchies (implicit animations on large trees).

Summarize findings with evidence from traces/logs.

4. Remediate

Apply targeted fixes:

  • Narrow state scope (@State/@Observable closer to leaf views).
  • Stabilize identities for ForEach and lists.
  • Move heavy work out of body (precompute, cache, @State).
  • Use equatable() or value wrappers for expensive subtrees.
  • Downsample images before rendering.
  • Reduce layout complexity or use fixed sizing where possible.

Common Code Smells (and Fixes)

Look for these patterns during code review.

Expensive formatters in body

var body: some View { let number = NumberFormatter() // slow allocation let measure = MeasurementFormatter() // slow allocation Text(measure.string(from: .init(value: meters, unit: .meters))) }

Prefer cached formatters in a model or a dedicated helper:

final class DistanceFormatter { static let shared = DistanceFormatter() let number = NumberFormatter() let measure = MeasurementFormatter() }

Computed properties that do heavy work

var filtered: [Item] { items.filter { $0.isEnabled } // runs on every body eval }

Prefer precompute or cache on change:

@State private var filtered: [Item] = [] // update filtered when inputs change

Sorting/filtering in body or ForEach

List { ForEach(items.sorted(by: sortRule)) { item in Row(item) } }

Prefer sort once before view updates:

let sortedItems = items.sorted(by: sortRule)

Inline filtering in ForEach

ForEach(items.filter { $0.isEnabled }) { item in Row(item) }

Prefer a prefiltered collection with stable identity.

Unstable identity

ForEach(items, id: \.self) { item in Row(item) }

Avoid id: \.self for non-stable values; use a stable ID.

Image decoding on the main thread

Image(uiImage: UIImage(data: data)!)

Prefer decode/downsample off the main thread and store the result.

Broad dependencies in observable models

@Observable class Model { var items: [Item] = [] } var body: some View { Row(isFavorite: model.items.contains(item)) }

Prefer granular view models or per-item state to reduce update fan-out.

5. 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.

References

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
5-Dim Analysis
Clarity9/10
Novelty7/10
Utility9/10
Completeness8/10
Maintainability8/10
Pros & Cons

Pros

  • Structured, actionable workflow; Concrete code examples and fixes; Guides through profiling when needed.

Cons

  • Requires user code/profile data; Reliant on user context quality; Advisory only, no automation.

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Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.

Copyright belongs to the original author Dimillian.