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axiom-xctrace-ref▌

charleswiltgen/axiom · updated Apr 8, 2026

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

Command-line interface for Instruments profiling. Enables headless performance analysis without GUI.

skill.md

xctrace CLI Reference

Command-line interface for Instruments profiling. Enables headless performance analysis without GUI.

Overview

xctrace is the CLI tool behind Instruments.app. Use it for:

  • Automated profiling in CI/CD pipelines
  • Headless trace collection without GUI
  • Programmatic trace analysis via XML export
  • Performance regression detection

Requires: Xcode 12+ (xctrace 12.0+). This reference tested with Xcode 26.2.

Quick Reference

# Record a 10-second CPU profile
xcrun xctrace record --instrument 'CPU Profiler' --attach 'MyApp' --time-limit 10s --output profile.trace

# Export to XML for analysis
xcrun xctrace export --input profile.trace --toc  # See available tables
xcrun xctrace export --input profile.trace --xpath '/trace-toc/run[@number="1"]/data/table[@schema="cpu-profile"]'

# List available instruments
xcrun xctrace list instruments

# List available templates
xcrun xctrace list templates

Recording Traces

Basic Recording

# Using an instrument (recommended for CLI automation)
xcrun xctrace record --instrument 'CPU Profiler' --attach 'AppName' --time-limit 10s --output trace.trace

# Using a template (may fail on export in Xcode 26+)
xcrun xctrace record --template 'Time Profiler' --attach 'AppName' --time-limit 10s --output trace.trace

Note: In Xcode 26+, use --instrument instead of --template for reliable export. Templates may produce traces with "Document Missing Template Error" on export.

Target Selection

# Attach to running process by name
xcrun xctrace record --instrument 'CPU Profiler' --attach 'MyApp' --time-limit 10s

# Attach to running process by PID
xcrun xctrace record --instrument 'CPU Profiler' --attach 12345 --time-limit 10s

# Profile all processes
xcrun xctrace record --instrument 'CPU Profiler' --all-processes --time-limit 10s

# Launch and profile
xcrun xctrace record --instrument 'CPU Profiler' --launch -- /path/to/app arg1 arg2

# Target specific device (simulator or physical)
xcrun xctrace record --instrument 'CPU Profiler' --device 'iPhone 17 Pro' --attach 'MyApp' --time-limit 10s
xcrun xctrace record --instrument 'CPU Profiler' --device 947DF45C-4ACB-4B3E-A043-DF2CD59A59B3 --all-processes --time-limit 10s

Recording Options

Flag Description
--output <path> Output .trace file path
--time-limit <time> Recording duration (e.g., 10s, 1m, 500ms)
--no-prompt Skip privacy warnings (use in automation)
--append-run Add run to existing trace
--run-name <name> Name the recording run

Core Instruments

CPU Profiler

CPU sampling for finding hot functions.

xcrun xctrace record --instrument 'CPU Profiler' --attach 'MyApp' --time-limit 10s --output cpu.trace

Schema: cpu-profile Columns: time, thread, process, core, thread-state, weight (cycles), stack

Allocations

Memory allocation tracking.

xcrun xctrace record --instrument 'Allocations' --attach 'MyApp' --time-limit 30s --output alloc.trace

Schema: allocations Use for: Finding memory growth, object counts, allocation patterns

Leaks

Memory leak detection.

xcrun xctrace record --instrument 'Leaks' --attach 'MyApp' --time-limit 30s --output leaks.trace

Schema: leaks Use for: Detecting unreleased memory, retain cycles

SwiftUI

SwiftUI view body analysis.

xcrun xctrace record --instrument 'SwiftUI' --attach 'MyApp' --time-limit 10s --output swiftui.trace

Schema: swiftui Use for: Finding excessive view updates, body re-evaluations

Swift Concurrency

Actor and Task analysis.

xcrun xctrace record --instrument 'Swift Tasks' --instrument 'Swift Actors' --attach 'MyApp' --time-limit 10s --output concurrency.trace

Schemas: swift-task, swift-actor Use for: Task scheduling, actor isolation, async performance

All Available Instruments

Activity Monitor          Audio Client              Audio Server
Audio Statistics          CPU Counters              CPU Profiler
Core Animation Activity   Core Animation Commits    Core Animation FPS
Core Animation Server     Core ML                   Data Faults
Data Fetches              Data Saves                Disk I/O Latency
Disk Usage                Display                   Filesystem Activity
Filesystem Suggestions    Foundation Models         Frame Lifetimes
GCD Performance           GPU                       HTTP Traffic
Hangs                     Hitches                   Leaks
Location Energy Model     Metal Application         Metal GPU Counters
Metal Performance Overview Metal Resource Events    Network Connections
Neural Engine             Points of Interest        Power Profiler
Processor Trace           RealityKit Frames         RealityKit Metrics
Runloops                  Sampler                   SceneKit Application
Swift Actors              Swift Tasks               SwiftUI
System Call Trace         System Load               Thread States
Time Profiler             VM Tracker                Virtual Memory Trace

Exporting Traces

Table of Contents

# See all available data tables in a trace
xcrun xctrace export --input trace.trace --toc

Output structure:

<trace-toc>
    <run number="1">
        <info>
            <target>...</target>
            <summary>...</summary>
        </info>
        <processes>...</processes>
        <data>
            <table schema="cpu-profile" .../>
            <table schema="thread-info"/>
            <table schema="process-info"/>
        </data>
    </run>
</trace-toc>

XPath Export

# Export specific table by schema
xcrun xctrace export --input trace.trace --xpath '/trace-toc/run[@number="1"]/data/table[@schema="cpu-profile"]'

# Export process info
xcrun xctrace export --input trace.trace --xpath '/trace-toc/run[@number="1"]/data/table[@schema="process-info"]'

# Export thread info
xcrun xctrace export --input trace.trace --xpath '/trace-toc/run[@number="1"]/data/table[@schema="thread-info"]'

CPU Profile Schema

<schema name="cpu-profile">
    <col><mnemonic>time</mnemonic><name>Sample Time</name></col>
    <col><mnemonic>thread</mnemonic><name>Thread</name></col>
    <col><mnemonic>process</mnemonic><name>Process</name></col>
    <col><mnemonic>core</mnemonic><name>Core</name></col>
    <col><mnemonic>thread-state</mnemonic><name>State</name></col>
    <col><mnemonic>weight</mnemonic><name>Cycles</name></col>
    <col
how to use axiom-xctrace-ref

How to use axiom-xctrace-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-xctrace-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-xctrace-ref

The skills CLI fetches axiom-xctrace-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-xctrace-ref

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

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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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

4.7★★★★★55 reviews
  • ★★★★★Yuki Kim· Dec 24, 2024

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

  • ★★★★★Carlos Menon· Dec 20, 2024

    axiom-xctrace-ref is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Shikha Mishra· Dec 4, 2024

    I recommend axiom-xctrace-ref for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Yash Thakker· Nov 23, 2024

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

  • ★★★★★Sakshi Patil· Nov 15, 2024

    Useful defaults in axiom-xctrace-ref — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Yuki Mensah· Nov 15, 2024

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

  • ★★★★★Benjamin Wang· Nov 11, 2024

    Keeps context tight: axiom-xctrace-ref is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Dhruvi Jain· Oct 14, 2024

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

  • ★★★★★Chaitanya Patil· Oct 6, 2024

    Registry listing for axiom-xctrace-ref matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Aanya Desai· Oct 6, 2024

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

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