axiom-profile-performance
axiom-profile-performance equips AI coding assistants with specialized capabilities for measuring and optimizing app performance on Apple platforms. It enables headless profiling workflows that automatically gather execution traces and surface performance bottlenecks without manual instrumentation. Built for modern Swift development, this skill integrates seamlessly with Claude Code and other AI-native development environments.
axiom-profile-performance is an MIT-licensed skill that enables headless profiling workflows for Apple platform apps. It automatically gathers execution traces and surfaces performance bottlenecks without requiring manual instrumentation or the Instruments GUI. The skill integrates with AI coding assistants to streamline performance measurement in modern Swift development environments.
AI-generated summary based on this skill's SKILL.md
Install
CharlesWiltgen/Axiom/axiom-profile-performance · repository language: Go
git clone https://github.com/CharlesWiltgen/Axiom
cp -r Axiom/axiom-codex/skills/axiom-profile-performance ~/.claude/skills/axiom-profile-performanceFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is axiom-profile-performance and how does it profile app performance headless?
axiom-profile-performance is an MIT-licensed skill that enables headless profiling workflows for Apple platform apps. It automatically gathers execution traces and surfaces performance bottlenecks without requiring manual instrumentation or the Instruments GUI. The skill integrates with AI coding assistants to streamline performance measurement in modern Swift development environments.
Can axiom-profile-performance detect CPU, memory, network, or battery bottlenecks?
Yes. axiom-profile-performance specializes in detecting CPU, memory, network, and battery bottlenecks in running apps. It provides automated trace collection and analysis to identify which resources are constraining performance, helping developers pinpoint the exact sources of degradation across all major performance dimensions.
How does axiom-profile-performance support performance regression testing?
axiom-profile-performance enables before/after trace comparison to identify performance regressions. By collecting traces at different points in development, you can compare execution profiles and detect when changes introduce new bottlenecks or degrade previously acceptable performance metrics.
Does axiom-profile-performance work with SwiftUI and concurrent tasks?
Yes. axiom-profile-performance includes specialized analysis for SwiftUI rendering performance and concurrency task performance issues, including actor-based concurrent code. This makes it well-suited for profiling modern Swift applications that rely on SwiftUI and structured concurrency patterns.
Can I use axiom-profile-performance for trace collection from the command line?
Yes. axiom-profile-performance supports command-line trace collection and analysis workflows, enabling automated performance profiling without the Instruments GUI. This makes it ideal for CI/CD pipelines, headless testing environments, and integration with AI-native development tools like Claude Code.
What does axiom-profile-performance provide in its performance reports?
axiom-profile-performance generates honest, actionable performance reports that include a support matrix. These reports surface concrete findings about bottlenecks and regressions, giving developers clear guidance on where to focus optimization efforts across their Apple platform applications.
SKILL.md
rendered from the published skill — quoted content, verbatim
> Note: This audit may use Bash commands to run builds, tests, or CLI tools.
Performance Profiler Agent
You profile apps headlessly and turn the result into an honest, actionable report. You lean on xcprof for the mechanics — bounded/gated recording, back-reference resolution, user-code attribution, and an honest per-family support matrix — and spend your attention on what the user should actually fix.
Core Principle
Measure honestly, then attribute to user code. xcprof never reports "no findings" when it means "couldn't measure" — it emits a per-family support matrix (available / partial / not_exportable / not_present). Read that matrix before you call anything clean. And never hand-grep exported XML: xcprof analyze --json has already resolved the id/ref back-references that defeat grep and filtered system frames from app code.
Prerequisites
command -v xcprof && xcprof doctor
doctor verifies xcrun xctrace and
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axiom-codex/skills/axiom-profile-performance/SKILL.md
axiom-codex/skills/axiom-profile-performance/agents/openai.yaml