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Compose Performance Audit

Community
hanamizuki
compose-performance-audit

Audit and improve Jetpack Compose runtime performance from code review and architecture. Use when asked to diagnose slow rendering, janky scrolling, excessive recompositions, or performance issues in Compose UI.

Overview

Publisherhanamizuki
Repositorysolopreneur
Skill namecompose-performance-audit
Stars
150
Forks
9
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Compose 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/hanamizuki/solopreneur.git /tmp/solopreneur
mkdir -p .claude/skills
cp -r /tmp/solopreneur/plugins/claude/android-dev/skills/compose-performance-audit .claude/skills/compose-performance-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compose 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 Compose 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 Compose 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.

Compose Performance Audit

Overview

Audit Jetpack Compose 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 Layout Inspector output or Perfetto traces.

1. Code-First Review

Collect:

  • Target Composable code.
  • Data flow: state, remember, derived state, ViewModel connections.
  • Symptoms and reproduction steps.

Focus on:

  • Recomposition storms from unstable parameters or broad state changes.
  • Unstable keys in LazyColumn/LazyRow (key churn, missing keys).
  • Heavy work in composition (formatting, sorting, filtering, object allocation).
  • Unnecessary recompositions (missing remember, unstable classes, lambdas).
  • Large images without proper sizing or async loading.
  • Layout thrash (deep nesting, intrinsic measurements, SubcomposeLayout misuse).

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:

  • Use Layout Inspector in Android Studio to see recomposition counts.
  • Enable Recomposition Highlights in Compose tooling.
  • Use Perfetto or System Trace for frame timing analysis.
  • Check Macrobenchmark results for startup/scroll metrics.

Ask for:

  • Layout Inspector screenshot showing recomposition counts.
  • Perfetto trace or System Trace export.
  • Device/OS/build configuration (debug vs release).

Important: Ensure profiling is done on a release build with R8 enabled. Debug builds have significant overhead.

3. Analyze and Diagnose

Prioritize likely Compose culprits:

  • Recomposition storms from unstable parameters or broad state changes.
  • Unstable keys in lazy lists (key churn, index-based keys).
  • Heavy work in composition (formatting, sorting, object allocation).
  • Missing remember causing recreations on every recomposition.
  • Large images without Modifier.size() constraints.
  • Unnecessary state reads in wrong composition phases.

Summarize findings with evidence from traces/Layout Inspector.

4. Remediate

Apply targeted fixes:

  • Stabilize parameters: Use @Stable or @Immutable annotations on data classes.
  • Stabilize keys: Use stable, unique IDs for LazyColumn/LazyRow items.
  • Defer state reads: Use derivedStateOf, lambda-based modifiers, or Modifier.drawBehind.
  • Remember expensive computations: Wrap in remember { } or remember(key) { }.
  • Skip recomposition: Extract stable composables, use key() to control identity.
  • Async image loading: Use Coil/Glide with proper sizing constraints.
  • Reduce layout complexity: Flatten hierarchies, avoid deep nesting.

Common Code Smells (and Fixes)

Unstable lambda captures

kotlin
// BAD: New lambda instance every recomposition
Button(onClick = { viewModel.doSomething(item) }) { ... }

// GOOD: Use remember or method reference
val onClick = remember(item) { { viewModel.doSomething(item) } }
Button(onClick = onClick) { ... }

Expensive work in composition

kotlin
// BAD: Sorting on every recomposition
@Composable
fun ItemList(items: List<Item>) {
    val sorted = items.sortedBy { it.name } // Runs every recomposition
    LazyColumn { items(sorted) { ... } }
}

// GOOD: Use remember with key
@Composable
fun ItemList(items: List<Item>) {
    val sorted = remember(items) { items.sortedBy { it.name } }
    LazyColumn { items(sorted) { ... } }
}

Missing keys in LazyColumn

kotlin
// BAD: Index-based identity (causes recomposition on list changes)
LazyColumn {
    items(items) { item -> ItemRow(item) }
}

// GOOD: Stable key-based identity
LazyColumn {
    items(items, key = { it.id }) { item -> ItemRow(item) }
}

Unstable data classes

kotlin
// BAD: Unstable (contains List, which is not stable)
data class UiState(
    val items: List<Item>,
    val isLoading: Boolean
)

// GOOD: Mark as Immutable if truly immutable
@Immutable
data class UiState(
    val items: ImmutableList<Item>, // kotlinx.collections.immutable
    val isLoading: Boolean
)

Reading state too early

kotlin
// BAD: State read during composition (recomposes whole tree)
@Composable
fun AnimatedBox(scrollState: ScrollState) {
    val offset = scrollState.value // Recomposes on every scroll
    Box(modifier = Modifier.offset(y = offset.dp)) { ... }
}

// GOOD: Defer state read to layout/draw phase
@Composable
fun AnimatedBox(scrollState: ScrollState) {
    Box(modifier = Modifier.offset {
        IntOffset(0, scrollState.value) // Read in layout phase
    }) { ... }
}

Object allocation in composition

kotlin
// BAD: Creates new Modifier chain every recomposition
Box(modifier = Modifier.padding(16.dp).background(Color.Red))

// GOOD for dynamic modifiers: Remember the modifier
val modifier = remember { Modifier.padding(16.dp).background(Color.Red) }
Box(modifier = modifier)

Stability Checklist

TypeStable by Default?Fix
Primitives (Int, String, Boolean)YesN/A
data class with stable fieldsYes*Ensure all fields are stable
List, Map, SetNoUse ImmutableList from kotlinx
Classes with var propertiesNoUse @Stable if externally stable
LambdasNoUse remember { }

5. Verify

Ask the user to:

  • Re-run Layout Inspector and compare recomposition counts.
  • Run Macrobenchmark and compare frame timing.
  • Test on a real device with release build.

Summarize the delta (recomposition count, frame drops, jank) if provided.

Outputs

Provide:

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

References

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 Compose Performance Audit AI skill do?

Audit and improve Jetpack Compose runtime performance from code review and architecture. Use when asked to diagnose slow rendering, janky scrolling, excessive recompositions, or performance issues in Compose UI.

Why use Compose Performance Audit on TypingMind?

Because you install it once and use it with any model. Compose 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 Compose Performance Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hanamizuki/solopreneur/tree/main/plugins/claude/android-dev/skills/compose-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 Compose 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 Compose Performance Audit?

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

Is the Compose Performance Audit AI skill free?

Yes. It is published on GitHub by hanamizuki 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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