Get started with the arm-performix agent skill for profiling and improving Arm workloads
Introduction
Understand the arm-performix skill
Install and enable the arm-performix skill
Best practices for prompting AI assistants to use the arm-performix skill
Use generated Arm Performix analysis reports to guide improvements
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  30 min
Last updated:  27 Jul 2026
Skill level:
  Introductory
Reading time:
  30 min
Last updated:
  27 Jul 2026
Author:Henry Wang, Arm
Arm IP:Neoverse
Tags: Performance and Architecture Linux macOS Windows Arm Performix GitHub Copilot MCP
Author:
  • Henry Wang, Arm
Arm IP:
Neoverse
Tags:
Performance and Architecture Linux macOS Windows Arm Performix GitHub Copilot MCP

Who is this for?

This is an introductory topic for developers who use an AI coding assistant with Agent Skills support, such as GitHub Copilot in VS Code or Claude Code, and want the arm-performix skill to drive Arm Performix profiling workflows without memorizing the apx command-line interface.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Install and enable the arm-performix skill in your AI assistant
  • Trigger the skill with phrasing that activates the profiling workflow
  • Provide the context the skill needs to profile (target, binary, workload)
  • Read the analysis report the skill produces and drive the improvement loop

Prerequisites

Before starting, you will need the following:

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll install the arm-performix skill and learn how to activate it with a performance-focused prompt. You’ll learn that you need to provide an Arm Neoverse target, an absolute binary path, a repeatable workload, and a profiling goal in your prompts. Then, you’ll interpret an example structured analysis report and learn how to use a report’s recommendations to guide measured improvements.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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How do I install the arm-performix skill using Git?
Clone the skills repository, then copy the arm-performix folder into your project’s skills directory.
Do I need to run the apx CLI myself, or does the skill handle it?
You don’t need to run apx CLI commands manually. The skill guides AI assistants to use the apx CLI by default. Ensure Arm Performix is installed and apx is available on the host PATH.
How do I write prompts to activate the arm-performix skill?
Use clear profiling intent, for example: “Profile this workload on Arm and find the hotspots”, “Why is this binary slow on my Arm Neoverse server?”, “Use Performix to check whether my hot loop uses vector instructions”, or “Investigate cache and translation lookaside buffer stalls on my Neoverse target”. Vague prompts are less effective.
What context should I include so the skill can profile correctly?
State the target (Arm Neoverse-based Linux instance), an absolute path to the binary to analyze, how to run the workload, and the profiling goal. Provide any run arguments the assistant needs to execute the binary.
What should I expect after the skill completes a profiling run?
Expect a structured analysis report with sections such as “Bottleneck Summary”, “Key Metrics”, “Hot Functions”, “Recommended Actions”, “Ruled Out”, and “Next Step”. Use “Recommended Actions” and “Next Step” to drive code changes, then ask the assistant to rerun the recipe to validate the result.
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