compose-performance-audit

new-silvermoon/awesome-android-agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/new-silvermoon/awesome-android-agent-skills --skill compose-performance-audit
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summary

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

skill.md

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

// 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

// 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

// 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

// 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

// 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

// 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

Type Stable by Default? Fix
Primitives (Int, String, Boolean) Yes N/A
data class with stable fields Yes* Ensure all fields are stable
List, Map, Set No Use ImmutableList from kotlinx
Classes with var properties No Use @Stable if externally stable
Lambdas No Use 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

how to use compose-performance-audit

How to use compose-performance-audit on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add compose-performance-audit
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/new-silvermoon/awesome-android-agent-skills --skill compose-performance-audit

The skills CLI fetches compose-performance-audit from GitHub repository new-silvermoon/awesome-android-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/compose-performance-audit

Reload or restart Cursor to activate compose-performance-audit. Access the skill through slash commands (e.g., /compose-performance-audit) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.847 reviews
  • Chinedu Liu· Dec 24, 2024

    Solid pick for teams standardizing on skills: compose-performance-audit is focused, and the summary matches what you get after install.

  • Isabella Park· Dec 20, 2024

    Keeps context tight: compose-performance-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Henry Sethi· Dec 20, 2024

    compose-performance-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Dec 12, 2024

    We added compose-performance-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chinedu Khanna· Dec 4, 2024

    I recommend compose-performance-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Omar Shah· Nov 23, 2024

    compose-performance-audit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • James Park· Nov 11, 2024

    compose-performance-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Henry Dixit· Nov 11, 2024

    Keeps context tight: compose-performance-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Omar Ndlovu· Nov 3, 2024

    We added compose-performance-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Omar Park· Oct 22, 2024

    Solid pick for teams standardizing on skills: compose-performance-audit is focused, and the summary matches what you get after install.

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