Instruments Profiling

CommunityPopular

steipete

instruments-profiling

Instruments/xctrace profiling: macOS/iOS traces, binaries, args, exports.

Overview

Publisher	steipete
Repository	`agent-scripts`
Skill name	`instruments-profiling`
Stars	6.6K
Forks	547
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by steipete on GitHub. Read the source before you install it.

Installation

Install the Instruments Profiling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/steipete/agent-scripts.git /tmp/agent-scripts
mkdir -p .claude/skills
cp -r /tmp/agent-scripts/skills/instruments-profiling .claude/skills/instruments-profiling

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Instruments Profiling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Instruments Profiling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Instruments Profiling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Instruments Profiling (macOS/iOS)

Use this skill when the user wants performance profiling or stack analysis for native apps. Focus: Time Profiler, xctrace CLI, and picking the correct binary/app instance.

Quick Start (CLI)

List templates: xcrun xctrace list templates
Record Time Profiler (launch):
- xcrun xctrace record --template 'Time Profiler' --time-limit 60s --output /tmp/App.trace --launch -- /path/To/App.app
Record Time Profiler (attach):
- Launch app yourself, get PID, then:
- xcrun xctrace record --template 'Time Profiler' --time-limit 60s --output /tmp/App.trace --attach <pid>
Open trace in Instruments:
- open -a Instruments /tmp/App.trace

Note: xcrun xctrace --help is not a valid subcommand. Use xcrun xctrace help record.

Picking the Correct Binary (Critical)

Gotcha: Instruments may profile the wrong app (e.g., one in /Applications) if LaunchServices resolves a different bundle. Use these rules:

Prefer direct binary path for deterministic launch:
- xcrun xctrace record ... --launch -- /path/App.app/Contents/MacOS/App
If launching .app, ensure it’s the intended bundle:
- open -n /path/App.app
- Verify with ps -p <pid> -o comm= -o command=
If both /Applications/App.app and a local build exist, explicitly target the local build path.
After launch, confirm the process path before trusting the trace.

Command Arguments (xctrace)

--template 'Time Profiler': template name from xctrace list templates.
--launch -- <cmd>: everything after -- is the target command (binary or app bundle).
--attach <pid|name>: attach to running process.
--output <path>: .trace output. If omitted, file saved in CWD.
--time-limit 60s|5m: set capture duration.
--device <name|UDID>: required for iOS device runs.
--target-stdout -: stream launched process stdout to terminal (useful for CLI tools).

Exporting Stacks (CLI)

Inspect trace tables:
- xcrun xctrace export --input /tmp/App.trace --toc
Export raw time-profile samples:
- xcrun xctrace export --input /tmp/App.trace --xpath '/trace-toc/run[@number="1"]/data/table[@schema="time-profile"]' --output /tmp/time-profile.xml
Post-process in a script (Python/Rust) to aggregate stacks.

Instruments UI Workflow

Template: Time Profiler
Use “Record” and capture the slow path (startup vs steady-state)
Call Tree tips:
- Hide System Libraries
- Invert Call Tree
- Separate by Thread
- Focus on hot frames and call counts

Gotchas & Fixes

Wrong app profiled: LaunchServices resolves installed app instead of local build.
- Fix: use direct binary path or --attach with known PID.
No samples / empty trace: App exits quickly or never hits work.
- Fix: longer capture, trigger workload during recording.
Privacy prompts: xctrace may need Developer Tools permission.
- Fix: System Settings → Privacy & Security → Developer Tools → allow Terminal/Xcode.
Large XML exports: time-profile exports are huge.
- Fix: filter with XPath and aggregate offline; don’t print to terminal.

iOS Specific Notes

Device: use xcrun xctrace list devices and --device <UDID>.
Launch via Xcode if needed; attach with xctrace --attach.
Ensure debug symbols for meaningful stacks.

Verification Checklist

Confirm trace process path matches target build.
Confirm stacks show expected app frames.
Capture covers the slow operation (startup/refresh).
Export stacks for automated diffing if optimizing.

Frequently asked questions

What does the Instruments Profiling AI skill do?

Instruments/xctrace profiling: macOS/iOS traces, binaries, args, exports.

Why use Instruments Profiling on TypingMind?

Because you install it once and use it with any model. Instruments Profiling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Instruments Profiling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/steipete/agent-scripts/tree/main/skills/instruments-profiling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Instruments Profiling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Instruments Profiling?

As many as you like. As long as a model supports skills, you can use Instruments Profiling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Instruments Profiling AI skill free?

Yes. It is published on GitHub by steipete under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Instruments Profiling (macOS/iOS)

Quick Start (CLI)

Picking the Correct Binary (Critical)

Command Arguments (xctrace)

Exporting Stacks (CLI)

Instruments UI Workflow

Gotchas & Fixes

iOS Specific Notes

Verification Checklist

Frequently asked questions

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