Performance Profiling
Meng ToCommunityCreated by Meng To
- Skill
- Guidelines
- Software development
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to performance profiling for Apple platform apps. It helps developers identify bottlenecks related to CPU usage, memory management, launch times, and energy consumption. By leveraging Xcode instruments, diagnostics, and MetricKit, the skill guides users through a repeatable workflow: identifying the performance category, profiling on physical hardware, applying targeted fixes, and verifying improvements with concrete measurements.
What it does
- Provides a decision tree to match performance issues with the correct diagnostic tool.
- Outlines a systematic workflow for profiling, fixing, and verifying performance improvements.
- Offers specific guidance for CPU, memory, launch time, and energy diagnostics.
- Recommends Xcode diagnostic settings to detect common runtime issues.
- Provides code patterns for integrating MetricKit to monitor production performance.
- Includes a review checklist for responsiveness, memory, launch, and energy best practices.
When to use it
- When investigating app hangs, stutters, or high CPU usage.
- When troubleshooting memory leaks, growth, or OOM crashes.
- When optimizing cold, warm, or resume launch times.
- When addressing battery drain, thermal throttling, or excessive network usage.
- When preparing an application for App Store performance readiness.
Advantages
- Promotes data-driven optimization by prioritizing real-device profiling.
- Reduces guesswork by mapping specific symptoms to dedicated profiling tools.
- Encourages sustainable performance through production monitoring with MetricKit.
- Provides actionable code examples for common performance pitfalls.
- Standardizes the process of verifying fixes before and after implementation.
Usage examples
- “My app is experiencing high CPU usage. How should I investigate?”
- The skill directs you to use the Time Profiler instrument, focus on CPU percent by function, and check the call tree weight to identify the specific code path causing the bottleneck.
- “How can I reduce my app's cold launch time?”
- The skill suggests using the App Launch instrument, reducing dynamic frameworks, removing static initializers, and deferring non-essential initialization code in the app's entry point.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/mengto/performance-profiling/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip performance-profiling.zip -d .agents/skills
Documentation by Meng To
Performance Profiling
Use this skill to diagnose Apple app performance issues systematically, pick the right profiling workflow, apply targeted fixes, and verify the change with real measurements.
Decision Tree
Choose the reference file before changing code:
What performance problem are you investigating?
+ App hangs, stutters, dropped frames, slow UI, high CPU
-> Read references/time-profiler.md
+ High memory, leaks, OOM crashes, growing footprint
-> Read references/memory-profiling.md
+ Slow cold launch, warm launch, resume, or time to first frame
-> Read references/launch-optimization.md
+ Battery drain, thermal throttling, background energy, network waste
-> Read references/energy-diagnostics.md
+ General "app feels slow"
-> Start with references/time-profiler.md, then references/memory-profiling.md
+ Pre-release performance audit
-> Read all reference files and use the review checklist belowQuick Reference
| Problem | Instrument / Tool | Key Metric | Reference |
|---|---|---|---|
| UI hangs over 250 ms | Time Profiler + Hangs | Hang duration, main thread stack | references/time-profiler.md |
| High CPU usage | Time Profiler | CPU percent by function, call tree weight | references/time-profiler.md |
| Memory leak | Leaks + Memory Graph | Leaked bytes, retain cycle paths | references/memory-profiling.md |
| Memory growth | Allocations | Live bytes, generation analysis | references/memory-profiling.md |
| Slow launch | App Launch | Time to first frame, pre-main, post-main | references/launch-optimization.md |
| Battery drain | Energy Log | Energy impact, CPU/GPU/network activity | references/energy-diagnostics.md |
| Thermal issues | Activity Monitor, Instruments | Thermal state transitions | references/energy-diagnostics.md |
| Network waste | Network profiler | Redundant fetches, payload size | references/energy-diagnostics.md |
Workflow
Identify the performance category from the user report, traces, logs, or code path.
Read only the matching reference file unless the issue is broad or unclear.
Prefer real device profiling with a Release build and representative data.
Inspect the code path named by the profile before proposing a fix.
Apply the smallest targeted fix that addresses the measured bottleneck.
Re-profile or add a repeatable measurement to confirm the improvement.
Profiling Ground Rules
Profile on device when possible; Simulator uses host CPU and memory.
Use Release configuration because optimizations can change hot paths.
Reproduce with representative data, not empty databases or toy assets.
Close unrelated apps to reduce noise during profiling.
Keep measurements before and after the fix so the outcome is concrete.
Add
os_signpostmarkers when a workflow needs ongoing timing visibility.
Xcode Diagnostics
Recommend relevant Scheme > Run > Diagnostics settings when they match the suspected issue:
| Setting | Use For |
|---|---|
| Main Thread Checker | UI work off the main thread |
| Thread Sanitizer | Data races and unsafe shared state |
| Address Sanitizer | Buffer overflows and use-after-free |
| Malloc Stack Logging | Allocation call stacks |
| Zombie Objects | Messages to deallocated objects |
MetricKit Hook
Suggest MetricKit for production monitoring of launch, responsiveness, memory, and diagnostics:
import MetricKit
final class PerformanceReporter: NSObject, MXMetricManagerSubscriber {
func startCollecting() {
MXMetricManager.shared.add(self)
}
func didReceive(_ payloads: [MXMetricPayload]) {
for payload in payloads {
if let launch = payload.applicationLaunchMetrics {
log("Resume time: \(launch.histogrammedResumeTime)")
}
if let responsiveness = payload.applicationResponsivenessMetrics {
log("Hang time: \(responsiveness.histogrammedApplicationHangTime)")
}
if let memory = payload.memoryMetrics {
log("Peak memory: \(memory.peakMemoryUsage)")
}
}
}
func didReceive(_ payloads: [MXDiagnosticPayload]) {
for payload in payloads {
if let hangs = payload.hangDiagnostics {
for hang in hangs {
log("Hang: \(hang.callStackTree)")
}
}
}
}
}Review Checklist
Responsiveness:
No synchronous work on the main thread over 100 ms.
No file I/O or network calls on the main thread.
Large Core Data or SwiftData fetches use background contexts.
Images decode off the main thread.
@MainActoris limited to code that truly needs UI access.
Memory:
No retain cycles in delegates, closures, observers, or async tasks.
Large resources are released when no longer visible.
Collections and caches are bounded.
autoreleasepoolis used in tight loops that create Objective-C objects.
Launch:
No heavy work in
init()of the@main Appstruct.Non-essential initialization is deferred.
Dynamic frameworks are minimized where practical.
No synchronous network calls occur during launch.
Energy:
Background tasks use the appropriate
BGTaskSchedulerrequest type.Location accuracy matches the product need.
Timers use tolerance so the system can coalesce wakeups.
Network requests are batched and cached where possible.
References
references/time-profiler.md: CPU profiling, hang detection, signpost API.references/memory-profiling.md: Allocations, Leaks, Memory Graph debugger.references/launch-optimization.md: Launch phases and cold/warm start optimization.references/energy-diagnostics.md: Battery, thermal state, and network efficiency.
Files
- ATTRIBUTION.mdAdded by AutoGineer859 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md6.0 KiB
- agents/openai.yaml204 B
- demo/PROMPT.md2.0 KiB
- demo/expected-output.md281 B
- demo/index.html3.8 KiB
- demo/input.md164 B
- demo/preview.jpg75.7 KiB
- references/energy-diagnostics.md9.8 KiB
- references/launch-optimization.md9.8 KiB
- references/memory-profiling.md8.8 KiB
- references/time-profiler.md6.4 KiB
references/time-profiler.md
Time Profiler & Hang Detection
Contents
Instruments Setup
Reading Time Profiler Results
Hang Detection
os_signpost API for Custom Profiling
Thread Performance Checker
Profiling Tips
Instruments Setup
Time Profiler Template
Xcode > Product > Profile (Cmd+I) - builds Release and opens Instruments
Choose Time Profiler template
Click record, reproduce the slow operation, stop recording
Key Settings
Recording Mode: Deferred (lower overhead, recommended for production-like profiling)
High Frequency: 10ms sampling for fine-grained analysis (default is 1ms)
Record Waiting Threads: Enable to see threads blocked on locks/IO
Reading Time Profiler Results
Call Tree Navigation
Weight: Total time spent in function + all children
Self Weight: Time in just that function (no children)
Symbol Name: Function/method being sampled
Essential Filters
| Filter | Purpose |
|---|---|
| Separate by Thread | Isolate main thread from background |
| Invert Call Tree | Show leaf functions first (where time is actually spent) |
| Hide System Libraries | Focus on your code |
| Flatten Recursion | Collapse recursive calls into one entry |
Typical Workflow
Separate by Thread - find Main Thread
Invert Call Tree - heaviest leaf functions appear at top
Hide System Libraries - focus on your functions
Click disclosure triangle to trace back through the call chain
Double-click a function to jump to source code
Hang Detection
What Counts as a Hang
| Duration | Classification | User Perception |
|---|---|---|
| < 100ms | Acceptable | Smooth |
| 100-250ms | Micro-hang | Slight stutter |
| 250ms-1s | Hang | Noticeable delay |
| > 1s | Severe hang | App feels broken |
| > 3s | Watchdog kill risk | System may terminate |
Using the Hangs Instrument
Open Instruments, choose Time Profiler (includes Hang detection lane)
Or use standalone Animation Hitches instrument for UI-specific stalls
Record and reproduce the interaction
Orange/red regions in the Hangs lane indicate hang duration
Click a hang to see the main thread call stack at that moment
Common Hang Causes and Fixes
Synchronous File I/O on Main Thread
// Bad - blocks main thread
func loadData() -> Data {
return try! Data(contentsOf: largeFileURL)
}
// Good - move to background
func loadData() async -> Data {
return try! await Task.detached {
try Data(contentsOf: largeFileURL)
}.value
}Synchronous Network Call
// Bad - URLSession.shared.dataTask is async but
// this pattern blocks main thread waiting for result
let data = try! Data(contentsOf: remoteURL)
// Good - use async/await
let (data, _) = try await URLSession.shared.data(from: remoteURL)Heavy Computation on Main Thread
// Bad - sorting/filtering large arrays on main thread
func updateUI() {
let sorted = hugeArray.sorted { $0.score > $1.score }
tableView.reloadData()
}
// Good - offload computation
func updateUI() async {
let sorted = await Task.detached {
hugeArray.sorted { $0.score > $1.score }
}.value
tableView.reloadData()
}Core Data / SwiftData Fetch on Main Thread
// Bad - fetching thousands of objects blocks UI
let items = try context.fetch(FetchDescriptor<Item>())
// Good - use background context (SwiftData)
let items = try await Task.detached {
let context = ModelContext(container)
return try context.fetch(FetchDescriptor<Item>())
}.valueImage Decoding on Main Thread
// Bad - decoding happens lazily on first display, blocking main thread
imageView.image = UIImage(contentsOfFile: path)
// Good - decode off main thread (iOS 15+)
let thumbnail = await UIImage(contentsOfFile: path)?
.byPreparingThumbnail(ofSize: targetSize)
imageView.image = thumbnail
// Good - using preparingForDisplay
let decoded = await UIImage(contentsOfFile: path)?
.byPreparingForDisplay()os_signpost API for Custom Profiling
Add signposts to measure specific operations in Instruments:
import os
extension OSSignpostID {
static let dataLoad = OSSignpostID(log: .performance)
}
extension OSLog {
static let performance = OSLog(
subsystem: Bundle.main.bundleIdentifier!,
category: "Performance"
)
}
// Mark intervals
func loadData() async throws -> [Item] {
let signpostID = OSSignpostID(log: .performance)
os_signpost(.begin, log: .performance, name: "DataLoad", signpostID: signpostID)
defer {
os_signpost(.end, log: .performance, name: "DataLoad", signpostID: signpostID)
}
// ... actual work
return items
}
// Mark events (single point)
os_signpost(.event, log: .performance, name: "CacheMiss")Viewing Signposts in Instruments
Add os_signpost instrument to your trace
Filter by subsystem/category to find your markers
Intervals show as bars with duration, events show as points
Thread Performance Checker
Enable in Scheme > Run > Diagnostics > Thread Performance Checker.
Detects at runtime:
Priority inversions: High-priority thread waiting on low-priority thread
Non-UI work on main thread: Disk I/O, network calls detected on main
Excessive thread creation: Spawning too many threads
Issues appear as purple runtime warnings in Xcode's Issue Navigator.
Profiling Tips
Profile What Matters
Always profile on physical device - Simulator has different CPU/memory characteristics
Use Release configuration - Debug builds disable compiler optimizations
Profile with realistic data - 10 items vs 10,000 items reveals different bottlenecks
Warm the app first - first run includes one-time setup that skews results
Interpreting Results
Focus on Self Weight to find actual bottlenecks (not just call tree weight)
A function with high weight but low self-weight is just a caller - dig deeper
Compare before/after traces to validate fixes
Look for repeated patterns - a function called 1000x at 1ms each = 1s hang
Quick Wins Checklist
[ ] Move all file I/O off main thread
[ ] Decode images asynchronously
[ ] Use
@MainActorsparingly - only for actual UI updates[ ] Batch database writes instead of single-row inserts
[ ] Cache expensive computations (JSON parsing, date formatting)
[ ] Use
DateFormatter/NumberFormatteras shared instances (creation is expensive)
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Meng To
- Publisher
- Meng To (Community)
- Source
- MengTo/Skills · agent-skills/codex/performance-profiling · commit 5ea1661
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Meng To.
Download ZIP
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