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Swiftui Performance Audit

CommunityPopular
steipete
swiftui-performance-audit

SwiftUI performance: render, scroll, CPU/memory, updates, layout, Instruments.

Overview

Publishersteipete
Repositoryagent-scripts
Skill nameswiftui-performance-audit
Stars
6.6K
Forks
547
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by steipete on GitHub. Read the source before you install it.

Installation

Install the Swiftui Performance Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/steipete/agent-scripts.git /tmp/agent-scripts
mkdir -p .claude/skills
cp -r /tmp/agent-scripts/skills/swiftui-performance-audit .claude/skills/swiftui-performance-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Swiftui Performance Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Swiftui Performance Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Swiftui Performance Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SwiftUI Performance Audit

Attribution: copied from @Dimillian’s Dimillian/Skills (2025-12-31).

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

swift
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:

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

Computed properties that do heavy work

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

Prefer precompute or cache on change:

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

Sorting/filtering in body or ForEach

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

Prefer sort once before view updates:

swift
let sortedItems = items.sorted(by: sortRule)

Inline filtering in ForEach

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

Prefer a prefiltered collection with stable identity.

Unstable identity

swift
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

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

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

Broad dependencies in observable models

swift
@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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Swiftui Performance Audit AI skill do?

SwiftUI performance: render, scroll, CPU/memory, updates, layout, Instruments.

Why use Swiftui Performance Audit on TypingMind?

Because you install it once and use it with any model. Swiftui Performance Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Swiftui Performance Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/steipete/agent-scripts/tree/main/skills/swiftui-performance-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Swiftui Performance Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Swiftui Performance Audit?

As many as you like. As long as a model supports skills, you can use Swiftui Performance Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Swiftui Performance Audit AI skill free?

Yes. It is published on GitHub by steipete under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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