axiom-metrickit-ref

charleswiltgen/axiom · updated Apr 8, 2026

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$npx skills add https://github.com/charleswiltgen/axiom --skill axiom-metrickit-ref
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summary

Complete API reference for collecting field performance metrics and diagnostics using MetricKit.

skill.md

MetricKit API Reference

Complete API reference for collecting field performance metrics and diagnostics using MetricKit.

Overview

MetricKit provides aggregated, on-device performance and diagnostic data from users who opt into sharing analytics. Data is delivered daily (or on-demand in development).

When to Use This Reference

Use this reference when:

  • Setting up MetricKit subscriber in your app
  • Parsing MXMetricPayload or MXDiagnosticPayload
  • Symbolicating MXCallStackTree crash data
  • Understanding background exit reasons (jetsam, watchdog)
  • Integrating MetricKit with existing crash reporters

For hang diagnosis workflows, see axiom-hang-diagnostics. For general profiling with Instruments, see axiom-performance-profiling. For memory debugging including jetsam, see axiom-memory-debugging.

Common Gotchas

  1. 24-hour delay — MetricKit data arrives once daily; it's not real-time debugging
  2. Call stacks require symbolication — MXCallStackTree frames are unsymbolicated; keep dSYMs
  3. Opt-in only — Only users who enable "Share with App Developers" contribute data
  4. Aggregated, not individual — You get counts and averages, not per-user traces
  5. Simulator doesn't work — MetricKit only collects on physical devices

iOS Version Support:

Feature iOS Version
Basic metrics (battery, CPU, memory) iOS 13+
Diagnostic payloads iOS 14+
Hang diagnostics iOS 14+
Launch diagnostics iOS 16+
Immediate delivery in dev iOS 15+

Part 1: Setup

Basic Integration

import MetricKit

class AppMetricsSubscriber: NSObject, MXMetricManagerSubscriber {

    override init() {
        super.init()
        MXMetricManager.shared.add(self)
    }

    deinit {
        MXMetricManager.shared.remove(self)
    }

    // MARK: - MXMetricManagerSubscriber

    func didReceive(_ payloads: [MXMetricPayload]) {
        for payload in payloads {
            processMetrics(payload)
        }
    }

    func didReceive(_ payloads: [MXDiagnosticPayload]) {
        for payload in payloads {
            processDiagnostics(payload)
        }
    }
}

Registration Timing

Register subscriber early in app lifecycle:

@main
struct MyApp: App {
    @StateObject private var metricsSubscriber = AppMetricsSubscriber()

    var body: some Scene {
        WindowGroup {
            ContentView()
        }
    }
}

Or in AppDelegate:

func application(_ application: UIApplication,
                 didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?) -> Bool {
    metricsSubscriber = AppMetricsSubscriber()
    return true
}

Development Testing

In iOS 15+, trigger immediate delivery via Debug menu:

Xcode > Debug > Simulate MetricKit Payloads

Or programmatically (debug builds only):

#if DEBUG
// Payloads delivered immediately in development
// No special code needed - just run and wait
#endif

Part 2: MXMetricPayload

MXMetricPayload contains aggregated performance metrics from the past 24 hours.

Payload Structure

func processMetrics(_ payload: MXMetricPayload) {
    // Time range for this payload
    let start = payload.timeStampBegin
    let end = payload.timeStampEnd

    // App version that generated this data
    let version = payload.metaData?.applicationBuildVersion

    // Access specific metric categories
    if let cpuMetrics = payload.cpuMetrics {
        processCPU(cpuMetrics)
    }

    if let memoryMetrics = payload.memoryMetrics {
        processMemory(memoryMetrics)
    }

    if let launchMetrics = payload.applicationLaunchMetrics {
        processLaunches(launchMetrics)
    }

    // ... other categories
}

CPU Metrics (MXCPUMetric)

func processCPU(_ metrics: MXCPUMetric) {
    // Cumulative CPU time
    let cpuTime = metrics.cumulativeCPUTime  // Measurement<UnitDuration>

    // iOS 14+: CPU instruction count
    if #available(iOS 14.0, *) {
        let instructions = metrics.cumulativeCPUInstructions  // Measurement<Unit>
    }
}

Memory Metrics (MXMemoryMetric)

func processMemory(_ metrics: MXMemoryMetric) {
    // Peak memory usage
    let peakMemory = metrics.peakMemoryUsage  // Measurement<UnitInformationStorage>

    // Average suspended memory
    let avgSuspended = metrics.averageSuspendedMemory  // MXAverage<UnitInformationStorage>
}

Launch Metrics (MXAppLaunchMetric)

func processLaunches(_ metrics: MXAppLaunchMetric) {
    // First draw (cold launch) histogram
    let firstDrawHistogram = metrics.histogrammedTimeToFirstDraw

    // Resume time histogram
    let resumeHistogram = metrics.histogrammedApplicationResumeTime

    // Optimized time to first draw (iOS 15.2+)
    if #available(iOS 15.2, *) {
        let optimizedLaunch = metrics.histogrammedOptimizedTimeToFirstDraw
    }

    // Parse histogram buckets
    for bucket in firstDrawHistogram.bucketEnumerator {
        if let bucket = bucket as? MXHistogramBucket<UnitDuration> {
            let start = bucket.bucketStart  // e.g., 0ms
            let end = bucket.bucketEnd      // e.g., 100ms
            let count = bucket.bucketCount  // Number of launches in this range
        }
    }
}

Application Exit Metrics (MXAppExitMetric) — iOS 14+

@available(iOS 14.0, *)
func processExits(_ metrics: MXAppExitMetric) {
    let fg = metrics.foregroundExitData
    let bg = metrics.backgroundExitData

    // Foreground (onscreen) exits
    let fgNormal = fg.cumulativeNormalAppExitCount
    let fgWatchdog = fg.cumulativeAppWatchdogExitCount
    let fgMemoryLimit = fg.cumulativeMemoryResourceLimitExitCount
    let fgMemoryPressure = fg.cumulativeMemoryPressureExitCount
    let fgBadAccess = fg.cumulativeBadAccessExitCount
    let fgIllegalInstruction = fg.cumulativeIllegalInstructionExitCount
    let fgAbnormal = fg.cumulativeAbnormalExitCount

    // Background exits
    let bgSuspended = bg.cumulativeSuspendedWithLockedFileExitCount
    let bgTaskTimeout = bg.
how to use axiom-metrickit-ref

How to use axiom-metrickit-ref 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 axiom-metrickit-ref
2

Execute installation command

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

$npx skills add https://github.com/charleswiltgen/axiom --skill axiom-metrickit-ref

The skills CLI fetches axiom-metrickit-ref from GitHub repository charleswiltgen/axiom 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/axiom-metrickit-ref

Reload or restart Cursor to activate axiom-metrickit-ref. Access the skill through slash commands (e.g., /axiom-metrickit-ref) 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

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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)
  • No comments yet — start the thread.
general reviews

Ratings

4.530 reviews
  • Nikhil Park· Dec 28, 2024

    axiom-metrickit-ref has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ganesh Mohane· Dec 24, 2024

    Solid pick for teams standardizing on skills: axiom-metrickit-ref is focused, and the summary matches what you get after install.

  • Jin Smith· Dec 24, 2024

    Solid pick for teams standardizing on skills: axiom-metrickit-ref is focused, and the summary matches what you get after install.

  • Alexander Brown· Nov 19, 2024

    axiom-metrickit-ref fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Rahul Santra· Nov 15, 2024

    We added axiom-metrickit-ref from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Jin Anderson· Nov 15, 2024

    We added axiom-metrickit-ref from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Alexander Agarwal· Oct 10, 2024

    We added axiom-metrickit-ref from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Pratham Ware· Oct 6, 2024

    axiom-metrickit-ref fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Jin Zhang· Oct 6, 2024

    axiom-metrickit-ref fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kaira Shah· Sep 1, 2024

    axiom-metrickit-ref reduced setup friction for our internal harness; good balance of opinion and flexibility.

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