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Debugging Instruments

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dpearson2699
debugging-instruments

Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.

Overview

Publisherdpearson2699
Repositoryswift-ios-skills
Skill namedebugging-instruments
Stars
1.2K
Forks
61
Bundled files
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  • 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.

  • 3 bundled files

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

  • Open source

    Published by dpearson2699 on GitHub. Read the source before you install it.

Installation

Install the Debugging Instruments 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.
    https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/debugging-instruments
  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/dpearson2699/swift-ios-skills.git /tmp/swift-ios-skills
mkdir -p .claude/skills
cp -r /tmp/swift-ios-skills/skills/debugging-instruments .claude/skills/debugging-instruments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Instruments 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 Debugging Instruments 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 Debugging Instruments 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.

Debugging and Instruments

Keep interactive graph and Instruments triage here. Route detailed .memgraph command-line ownership/growth analysis and ETTrace work to their focused skills.

Contents

LLDB Debugging

Start with a small, repeatable workflow:

  1. Reproduce in a Debug build and stop at the narrowest useful breakpoint.
  2. Inspect locals without executing code, then capture the current stack.
  3. Move to the relevant frame or thread and verify the failing state.
  4. Add a condition or watchpoint only when the bad transition is still unclear.
text
(lldb) br set -f ViewModel.swift -l 42     # Stop at file and line
(lldb) v myLocal                           # Inspect without executing code
(lldb) po myObject                         # Use debugDescription when needed
(lldb) bt all                              # Capture every thread's backtrace
(lldb) frame select 3                      # Inspect a relevant frame
(lldb) br modify 1 -c "count > 10"         # Narrow a noisy breakpoint
(lldb) w set v self.score                  # Stop on an unexpected write

Use v over po when you only need a local variable value — it does not execute code and cannot trigger side effects. Expression evaluation can execute or mutate program state, and hardware watchpoints are scarce, so use both deliberately.

Load references/lldb-patterns.md for the complete inspection, breakpoint/logpoint, expression, watchpoint, thread navigation, and symbolic-breakpoint command tables.

Memory Debugging

Memory Graph Debugger Workflow

  1. Run the app in Debug configuration.
  2. Reproduce the suspected leak (navigate to a screen, then back).
  3. Tap the Memory Graph button in Xcode's debug bar.
  4. Look for purple warning icons — these indicate leaked objects.
  5. Select a leaked object to see its reference graph and backtrace.

Enable Malloc Stack Logging (Scheme > Diagnostics) before running so the Memory Graph shows allocation backtraces.

Common Retain Cycle Patterns

Closure capturing self strongly:

swift
// LEAK — closure holds strong reference to self
class ProfileViewModel {
    var onUpdate: (() -> Void)?

    func startObserving() {
        onUpdate = {
            self.refresh()  // strong capture of self
        }
    }
}

// FIXED — use [weak self]
func startObserving() {
    onUpdate = { [weak self] in
        self?.refresh()
    }
}

Strong delegate reference:

swift
// LEAK — strong delegate creates a cycle
protocol DataDelegate: AnyObject {
    func didUpdate()
}

class DataManager {
    var delegate: DataDelegate?  // should be weak
}

// FIXED — weak delegate
class DataManager {
    weak var delegate: DataDelegate?
}

Timer retaining target:

swift
// LEAK — Timer.scheduledTimer retains its target
timer = Timer.scheduledTimer(
    timeInterval: 1.0, target: self,
    selector: #selector(tick), userInfo: nil, repeats: true
)

// FIXED — use closure-based API with [weak self]
timer = Timer.scheduledTimer(withTimeInterval: 1.0, repeats: true) { [weak self] _ in
    self?.tick()
}

Instruments: Allocations and Leaks

  • Allocations template: Track memory growth over time. Use the "Mark Generation" feature to isolate allocations created between user actions (e.g., open/close a screen).
  • Leaks template: Detects leaked allocations, including isolated retain cycles the process can no longer reach. Run alongside Allocations for a complete picture.
  • Filter by your app's module name to exclude system allocations.

For leak or memory-growth triage, pair the tools: use Allocations Mark Generation before and after the reproduction step to prove retained growth, then use Memory Graph Debugger to inspect object ownership and Malloc Stack Logging to recover allocation call stacks.

Malloc Stack Logging

Enable in Scheme > Run > Diagnostics > Malloc Stack Logging. This records allocation backtraces so the Memory Graph Debugger, Allocations instrument, and exported .memgraph files can show where objects were created.

bash
# Inspect an exported memory graph from Xcode or Instruments
leaks MyApp.memgraph

Hang Diagnostics

Identifying Main Thread Hangs

For discrete interactions, delays under 100 ms are rarely noticeable. Apple developer tools commonly report main-run-loop busy periods over 250 ms, but that reporting threshold is not a product target: a few hundred milliseconds can still feel unresponsive. Common detection tools:

  • Thread Checker (Xcode Diagnostics): warns about non-main-thread UI calls
  • Thread Performance Checker: reports priority inversions while debugging
  • On-device Hang Detection: Developer Settings reports hangs from device use
  • Time Profiler / CPU Profiler / Hitches: profile reproducible hangs
  • os_signpost and OSSignposter: mark intervals for Instruments
  • MetricKit hang diagnostics: production hang detection (see metrickit skill for HangDiagnostic and iOS 26 compatibility)
swift
import os

let signposter = OSSignposter(subsystem: "com.example.app", category: "DataLoad")

func loadData() async {
    let state = signposter.beginInterval("loadData")
    let result = await fetchFromNetwork()
    signposter.endInterval("loadData", state)
    process(result)
}

Using the Time Profiler

  1. Product > Profile (Cmd+I) to launch Instruments.
  2. Select the Time Profiler template.
  3. Record while reproducing the slow interaction.
  4. Focus on the main thread — sort by "Weight" to find hot paths.
  5. Check "Hide System Libraries" to see only your code.
  6. Double-click a heavy frame to jump to source.

Common Hang Causes

CauseSymptomFix
Synchronous I/O on main threadNetwork/file reads block UIMove to Task { } or background actor
Lock contentionMain thread waiting on a lock held by background workUse actors or reduce lock scope
Layout thrashingRepeated layoutSubviews callsBatch layout changes, avoid forced layout
JSON parsing large payloadsUI freezes during data loadParse on a background thread
Synchronous image decodingScroll jank on image-heavy listsUse AsyncImage or decode off main thread

Build Failure Triage

Reading Compiler Diagnostics

  • Start from the first error — subsequent errors are often cascading.
  • Search for the error code (e.g., error: cannot convert) in the build log.
  • Use Report Navigator (Cmd+9) for the full build log with timestamps.

SPM Dependency Resolution

text
# Common: version conflict
error: Dependencies could not be resolved because root depends on 'Package' 1.0.0..<2.0.0

# Fix: check Package.resolved and update version ranges
# Reset package caches if needed:
rm -rf ~/Library/Caches/org.swift.swiftpm
rm -rf .build
swift package resolve

Module Not Found / Linker Errors

ErrorCheck
No such module 'Foo'Target membership, import paths, framework search paths
Undefined symbolLinking phase missing framework, wrong architecture
duplicate symbolTwo targets define same symbol; check for ObjC naming collisions

Build settings to inspect first:

  • FRAMEWORK_SEARCH_PATHS
  • OTHER_LDFLAGS
  • SWIFT_INCLUDE_PATHS
  • BUILD_LIBRARY_FOR_DISTRIBUTION (for XCFrameworks)

Instruments Overview

Template Selection Guide

TemplateUse When
Time ProfilerCPU is high, UI feels slow, need to find hot code paths
AllocationsMemory grows over time, need to track object lifetimes
LeaksSuspect retain cycles or abandoned objects
NetworkInspecting HTTP request/response timing and payloads
SwiftUIProfiling view body evaluations and update frequency
Animation Hitches / Core Animation instrumentsFrame drops, hitches, blending, and commit/render work
Power ProfilerBattery drain, thermal pressure, background energy impact
File ActivityExcessive disk I/O, slow file operations
System TraceThread scheduling, syscalls, virtual memory faults

xctrace CLI for CI Profiling

bash
# Record a trace from the command line
xcrun xctrace record --device "My iPhone" \
    --template "Time Profiler" \
    --instrument "Allocations" \
    --output profile.trace \
    --launch -- /path/to/MyApp.app

# Export trace data as XML for automated analysis
xcrun xctrace export --input profile.trace --xpath '/trace-toc/run/data/table'

# List available templates
xcrun xctrace list templates

# List connected devices
xcrun xctrace list devices

Use one --template per recording; add extra instruments with --instrument. Use xctrace in CI pipelines to catch performance regressions automatically. Compare exported metrics between builds.

Common Mistakes

DON'T: Use print() for debugging instead of os.Logger

Use Logger for levels, privacy metadata, and subsystem/category filtering; .debug remains in memory and is not persisted in release builds.

swift
// WRONG — unstructured and not filterable by subsystem/category
print("user tapped button, state: \(viewModel.state)")
print("network response: \(data)")

// CORRECT — structured logging with Logger
import os

let logger = Logger(subsystem: "com.example.app", category: "UI")

logger.debug("Button tapped, state: \(viewModel.state, privacy: .public)")
logger.info("Network response received, bytes: \(data.count)")

DON'T: Forget to enable Malloc Stack Logging before memory debugging

swift
// WRONG — open Memory Graph without enabling Malloc Stack Logging
// Result: leaked objects visible but no allocation backtrace

// CORRECT — enable BEFORE running:
// Scheme > Run > Diagnostics > check "Malloc Stack Logging: All Allocations"
// Then run, reproduce the leak, and open Memory Graph

DON'T: Debug optimized code expecting full variable visibility

swift
// WRONG — profiling with Debug build, debugging with Release build
// Debug builds: extra runtime checks distort perf measurements
// Release builds: variables show as "<optimized out>" in debugger

// CORRECT approach:
// Debugging: use Debug configuration (full symbols, no optimization)
// Profiling: use Release configuration (realistic performance)

DON'T: Stop on every loop iteration without conditional breakpoints

swift
// WRONG — breakpoint on line inside loop, stops 10,000 times
for item in items {
    process(item)  // breakpoint here stops on EVERY item
}

// CORRECT — use a conditional breakpoint:
// (lldb) br set -f MyFile.swift -l 42 -c "item.id == targetID"
// Or in Xcode: right-click breakpoint > Edit > add Condition

DON'T: Ignore Thread Sanitizer warnings

Thread Sanitizer (TSan) warnings indicate data races that may only crash intermittently. Treat them as real bugs unless you have isolated a tool issue.

swift
// WRONG — ignoring TSan warning about concurrent access
var cache: [String: Data] = [:]  // accessed from multiple threads

// CORRECT — protect shared mutable state
actor CacheActor {
    var cache: [String: Data] = [:]

    func get(_ key: String) -> Data? { cache[key] }
    func set(_ key: String, _ value: Data) { cache[key] = value }
}

Enable TSan: Scheme > Run > Diagnostics > Thread Sanitizer. For iOS, iPadOS, tvOS, visionOS, and watchOS apps, run TSan in Simulator; Apple documents device support only for 64-bit macOS apps.

Review Checklist

  • Using os.Logger instead of print() for diagnostic output
  • Malloc Stack Logging enabled before memory debugging sessions
  • Memory Graph Debugger checked after dismiss/dealloc flows
  • Delegates declared as weak var to prevent retain cycles
  • Closures stored as properties use [weak self] capture lists
  • Timers use closure-based API with [weak self]
  • Thread Sanitizer enabled in Simulator test schemes for race triage
  • No synchronous I/O or heavy computation on the main thread
  • Time Profiler run on Release build for performance baselines
  • Build failures triaged from the first error in the build log
  • OSSignposter used for custom performance intervals
  • Conditional breakpoints used for loop/collection debugging

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 Debugging Instruments AI skill do?

Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.

Why use Debugging Instruments on TypingMind?

Because you install it once and use it with any model. Debugging Instruments 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 Debugging Instruments in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/debugging-instruments. 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 Debugging Instruments?

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 Debugging Instruments?

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

Is the Debugging Instruments AI skill free?

It is published on GitHub by dpearson2699. Check the repository for licensing terms. 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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