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
- Classify the symptom: slow rendering, janky scrolling, high CPU, memory growth, hangs, or excessive view updates.
- If code is available, start with a code-first review using
references/code-smells.md.
- If code is not available, ask for the smallest useful slice: target view, data flow, reproduction steps, and deployment target.
- If code review is inconclusive or runtime evidence is required, guide the user through profiling with
references/profiling-intake.md.
- 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
1---2name: guide-swiftui-performance-audit3description: 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.4license: MIT5---6
7> **Guide Skill** — This is an expert workflow/pattern guide, not API reference documentation.
8> Originally from [Dimillian/Skills](https://github.com/Dimillian/Skills) by Thomas Ricouard. MIT License.
9
10
11# SwiftUI Performance Audit
12
13## Quick start
14
15Use this skill to diagnose SwiftUI performance issues from code first, then request profiling evidence when code review alone cannot explain the symptoms.
16
17## Workflow
18
191. Classify the symptom: slow rendering, janky scrolling, high CPU, memory growth, hangs, or excessive view updates.
202. If code is available, start with a code-first review using `references/code-smells.md`.
213. If code is not available, ask for the smallest useful slice: target view, data flow, reproduction steps, and deployment target.
224. If code review is inconclusive or runtime evidence is required, guide the user through profiling with `references/profiling-intake.md`.
235. Summarize likely causes, evidence, remediation, and validation steps using `references/report-template.md`.
24
25## 1. Intake
26
27Collect:
28- Target view or feature code.
29- Symptoms and exact reproduction steps.
30- Data flow: `@State`, `@Binding`, environment dependencies, and observable models.
31- Whether the issue shows up on device or simulator, and whether it was observed in Debug or Release.
32
33Ask the user to classify the issue if possible:
34- CPU spike or battery drain
35- Janky scrolling or dropped frames
36- High memory or image pressure
37- Hangs or unresponsive interactions
38- Excessive or unexpectedly broad view updates
39
40For the full profiling intake checklist, read `references/profiling-intake.md`.
41
42## 2. Code-First Review
43
44Focus on:
45- Invalidation storms from broad observation or environment reads.
46- Unstable identity in lists and `ForEach`.
47- Heavy derived work in `body` or view builders.
48- Layout thrash from complex hierarchies, `GeometryReader`, or preference chains.
49- Large image decode or resize work on the main thread.
50- Animation or transition work applied too broadly.
51
52Use `references/code-smells.md` for the detailed smell catalog and fix guidance.
53
54Provide:
55- Likely root causes with code references.
56- Suggested fixes and refactors.
57- If needed, a minimal repro or instrumentation suggestion.
58
59## 3. Guide the User to Profile
60
61If code review does not explain the issue, ask for runtime evidence:
62- A trace export or screenshots of the SwiftUI timeline and Time Profiler call tree.
63- Device/OS/build configuration.
64- The exact interaction being profiled.
65- Before/after metrics if the user is comparing a change.
66
67Use `references/profiling-intake.md` for the exact checklist and collection steps.
68
69## 4. Analyze and Diagnose
70
71- Map the evidence to the most likely category: invalidation, identity churn, layout thrash, main-thread work, image cost, or animation cost.
72- Prioritize problems by impact, not by how easy they are to explain.
73- Distinguish code-level suspicion from trace-backed evidence.
74- Call out when profiling is still insufficient and what additional evidence would reduce uncertainty.
75
76## 5. Remediate
77
78Apply targeted fixes:
79- Narrow state scope and reduce broad observation fan-out.
80- Stabilize identities for `ForEach` and lists.
81- 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.
82- Use `equatable()` only when equality is cheaper than recomputing the subtree and the inputs are truly value-semantic.
83- Downsample images before rendering.
84- Reduce layout complexity or use fixed sizing where possible.
85
86Use `references/code-smells.md` for examples, Observation-specific fan-out guidance, and remediation patterns.
87
88## 6. Verify
89
90Ask the user to re-run the same capture and compare with baseline metrics.
91Summarize the delta (CPU, frame drops, memory peak) if provided.
92
93## Outputs
94
95Provide:
96- A short metrics table (before/after if available).
97- Top issues (ordered by impact).
98- Proposed fixes with estimated effort.
99
100Use `references/report-template.md` when formatting the final audit.
101
102## References
103
104- Profiling intake and collection checklist: `references/profiling-intake.md`
105- Common code smells and remediation patterns: `references/code-smells.md`
106- Audit output template: `references/report-template.md`
107- Add Apple documentation and WWDC resources under `references/` as they are supplied by the user.
108- Optimizing SwiftUI performance with Instruments: `references/optimizing-swiftui-performance-instruments.md`
109- Understanding and improving SwiftUI performance: `references/understanding-improving-swiftui-performance.md`
110- Understanding hangs in your app: `references/understanding-hangs-in-your-app.md`
111- Demystify SwiftUI performance (WWDC23): `references/demystify-swiftui-performance-wwdc23.md`
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