metrickit-diagnostics▌
dpearson2699/swift-ios-skills · updated Apr 8, 2026
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Collect aggregated performance metrics and crash diagnostics from production
- ›devices using MetricKit. The framework delivers daily metric payloads (CPU,
- ›memory, launch time, hang rate, animation hitches, network usage) and
- ›immediate diagnostic payloads (crashes, hangs, disk-write exceptions) with
- ›full call-stack trees for triage.
MetricKit Diagnostics
Collect aggregated performance metrics and crash diagnostics from production devices using MetricKit. The framework delivers daily metric payloads (CPU, memory, launch time, hang rate, animation hitches, network usage) and immediate diagnostic payloads (crashes, hangs, disk-write exceptions) with full call-stack trees for triage.
Contents
- Subscriber Setup
- Receiving Metric Payloads
- Receiving Diagnostic Payloads
- Key Metrics
- Call Stack Trees
- Custom Signpost Metrics
- Exporting and Uploading Payloads
- Extended Launch Measurement
- Xcode Organizer Integration
- Common Mistakes
- Review Checklist
- References
Subscriber Setup
Register a subscriber as early as possible — ideally in
application(_:didFinishLaunchingWithOptions:) or App.init. MetricKit
starts accumulating reports after the first access to MXMetricManager.shared.
import MetricKit
final class MetricsSubscriber: NSObject, MXMetricManagerSubscriber {
static let shared = MetricsSubscriber()
func subscribe() {
MXMetricManager.shared.add(self)
}
func unsubscribe() {
MXMetricManager.shared.remove(self)
}
func didReceive(_ payloads: [MXMetricPayload]) {
// Handle daily metrics
}
func didReceive(_ payloads: [MXDiagnosticPayload]) {
// Handle diagnostics (crashes, hangs, disk writes)
}
}
UIKit Registration
func application(
_ application: UIApplication,
didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?
) -> Bool {
MetricsSubscriber.shared.subscribe()
return true
}
SwiftUI Registration
@main
struct MyApp: App {
init() {
MetricsSubscriber.shared.subscribe()
}
var body: some Scene {
WindowGroup { ContentView() }
}
}
Receiving Metric Payloads
MXMetricPayload arrives approximately once per 24 hours containing
aggregated metrics. The array may contain multiple payloads if prior
deliveries were missed.
func didReceive(_ payloads: [MXMetricPayload]) {
for payload in payloads {
let begin = payload.timeStampBegin
let end = payload.timeStampEnd
let version = payload.latestApplicationVersion
// Persist raw JSON before processing
let jsonData = payload.jsonRepresentation()
persistPayload(jsonData, from: begin, to: end)
processMetrics(payload)
}
}
Availability: MXMetricPayload — iOS 13.0+, macOS 10.15+, visionOS 1.0+
Receiving Diagnostic Payloads
MXDiagnosticPayload delivers crash, hang, CPU exception, disk-write, and
app-launch diagnostics. On iOS 15+ and macOS 12+, diagnostics arrive
immediately rather than bundled with the daily report.
func didReceive(_ payloads: [MXDiagnosticPayload]) {
for payload in payloads {
let jsonData = payload.jsonRepresentation()
persistPayload(jsonData)
if let crashes = payload.crashDiagnostics {
for crash in crashes {
handleCrash(crash)
}
}
if let hangs = payload.hangDiagnostics {
for hang in hangs {
handleHang(hang)
}
}
if let diskWrites = payload.diskWriteExceptionDiagnostics {
for diskWrite in diskWrites {
handleDiskWrite(diskWrite)
}
}
if let cpuExceptions = payload.cpuExceptionDiagnostics {
for cpuException in cpuExceptions {
handleCPUException(cpuException)
}
}
if let launchDiags = payload.appLaunchDiagnostics {
for launchDiag in launchDiags {
handleSlowLaunch(launchDiag)
}
}
}
}
Availability: MXDiagnosticPayload — iOS 14.0+, macOS 12.0+, visionOS 1.0+
Key Metrics
Launch Time — MXAppLaunchMetric
if let launch = payload.applicationLaunchMetrics {
let firstDraw = launch.histogrammedTimeToFirstDraw
let optimized = launch.histogrammedOptimizedTimeToFirstDraw
let resume = launch.histogrammedApplicationResumeTime
let extended = launch.histogrammedExtendedLaunch
}
Run Time — MXAppRunTimeMetric
if let runTime = payload.applicationTimeMetrics {
let fg = runTime.cumulativeForegroundTime // Measurement<UnitDuration>
let bg = runTime.cumulativeBackgroundTime
let bgAudio = runTime.cumulativeBackgroundAudioTime
let bgLocation = runTime.cumulativeBackgroundLocationTime
}
CPU, Memory, and Responsiveness
if let cpu = payload.cpuMetrics {
let cpuTime = cpu.cumulativeCPUTime // Measurement<UnitDuration>
}
if let memory = payload.memoryMetrics {
let peakMemory = memory.peakMemoryUsage // Measurement<UnitInformationStorage>
}
if let responsiveness = payload.applicationResponsivenessMetrics {
let hangTime = responsiveness.histogrammedApplicationHangTime
}
if let animation = payload.animationMetrics {
let scrollHitchRate = animation.scrollHitchTimeRatio // Measurement<Unit>
}
Network and Cellular
if let network = payload.networkTransferMetrics {
let wifiUp = network.cumulativeWifiUpload // Measurement<UnitInformationStorage>
let wifiDown = network.cumulativeWifiDownload
let cellUp = network.cumulativeCellularUpload
let cellDown = netwHow to use metrickit-diagnostics on Cursor
AI-first code editor with Composer
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 metrickit-diagnostics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches metrickit-diagnostics from GitHub repository dpearson2699/swift-ios-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate metrickit-diagnostics. Access the skill through slash commands (e.g., /metrickit-diagnostics) 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
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★54 reviews- ★★★★★Olivia Gill· Dec 28, 2024
Solid pick for teams standardizing on skills: metrickit-diagnostics is focused, and the summary matches what you get after install.
- ★★★★★Neel Farah· Dec 20, 2024
Useful defaults in metrickit-diagnostics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Pratham Ware· Dec 8, 2024
metrickit-diagnostics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Lucas Perez· Dec 8, 2024
I recommend metrickit-diagnostics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hiroshi Brown· Dec 8, 2024
metrickit-diagnostics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Mei Bansal· Dec 4, 2024
Keeps context tight: metrickit-diagnostics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yash Thakker· Nov 27, 2024
Registry listing for metrickit-diagnostics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chinedu Okafor· Nov 27, 2024
Keeps context tight: metrickit-diagnostics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Olivia Rao· Nov 27, 2024
Useful defaults in metrickit-diagnostics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mia Chen· Nov 23, 2024
I recommend metrickit-diagnostics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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