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ios-instruments-performance-cli

@daispacy/py-claude-marketplace
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Use Xcode Instruments command line tools to analyze iOS app performance, detect memory leaks, optimize launch times, monitor CPU usage, and identify performance bottlenecks for the iOS project

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SKILL.md

name ios-instruments-performance-cli
description Use Xcode Instruments command line tools to analyze iOS app performance, detect memory leaks, optimize launch times, monitor CPU usage, and identify performance bottlenecks for the iOS project

iOS Instruments Performance CLI

Instructions

When helping with iOS app performance optimization using Instruments command line tools:

1. Setup Analysis Environment

CRITICAL: Always use device UUID, never device names

  • Get device UUID: xcrun simctl list devices available | grep "iPhone"
  • Device names like "iPhone 17 Pro" are ambiguous and will fail
  • Use UUID format: F464E766-555C-4B95-B8CC-763702A70791

Clean installation state (REQUIRED)

  • Always uninstall app before profiling: xcrun simctl uninstall $DEVICE_UUID <bundle id>
  • Multiple app installations cause "process is ambiguous" errors
  • Alternative: Completely erase simulator for cleanest state

Build configuration

  • Build in Release mode for accurate performance measurements

2. Choose Appropriate Instrument Template

Use xcrun xctrace list templates to see available templates:

  • App Launch - Essential for launch time optimization (most common)
  • Time Profiler - CPU performance analysis
  • Allocations - Memory usage tracking
  • Leaks - Memory leak detection
  • Network - API calls and network activity
  • Animation Hitches - UI performance issues

3. Run Performance Analysis

Standard workflow:

DEVICE_UUID="F464E766-555C-4B95-B8CC-763702A70791" # this is sample uuid, run command line to get exist uuid
xcrun simctl uninstall $DEVICE_UUID <bundle id>
xcrun simctl install $DEVICE_UUID /path/to/<app name>.app
sleep 2
xcrun xctrace record --template "App Launch" --device $DEVICE_UUID \
  --launch -- /path/to/<app name>.app 2>&1

Important notes:

  • Trace files auto-generate names like Launch_<app name>.app_2025-10-30_3.55.40 PM_39E6A410.trace
  • --output parameter may be ignored; accept auto-generated names
  • Wait 2 seconds after installation before profiling
  • Use 2>&1 to capture all output
  • Recording duration: 10-30s for launch, 2-5m for other analyses

4. Analyze Results

Finding trace files:

ls -lt *.trace | head -1  # Most recent trace
find . -name "*.trace" -type d -mmin -5  # Files from last 5 minutes

Analysis approaches:

  • CLI export often fails - "trace is malformed" errors are common

  • Recommended: Open in Instruments.app GUI for reliable analysis

    open -a Instruments.app Launch_<app name>*.trace
    
  • Parse signpost markers for performance metrics

  • Identify bottlenecks in launch sequence, memory allocations, CPU hotspots

5. Common Optimization Patterns

Based on successful optimizations:

  • Lazy initialization - Defer expensive dependency resolution
  • Background operations - Move non-critical setup off main thread
  • Deferred bindings - Set up subscriptions after UI appears
  • Font loading - Register fonts asynchronously
  • Method swizzling - Only essential swizzles during launch

Critical Pitfalls to Avoid

❌ Using device names instead of UUIDs ❌ Skipping clean installation step ❌ Building in Debug mode ❌ Trying to export trace immediately after recording ❌ Assuming --output path will be used ❌ Not waiting between installation and profiling

Reference Documentation

See examples in this skill directory for:

  • Detailed command examples with correct syntax
  • Complete troubleshooting guide with solutions
  • Real-world lessons learned
  • Production-ready workflow scripts