What is native-app-profiling?

A command-line workflow for measuring CPU use in native macOS and iOS apps. It records Time Profiler data with Apple’s xctrace tool and helps identify which code paths take the most time.

Use it to record a running or newly launched app, export timing samples, translate stack frames into readable function names, and locate CPU hotspots.

It helps find performance bottlenecks without opening Instruments, Apple’s graphical profiling app. This makes slow code paths easier to inspect from a terminal or automated workflow.

native-app-profiling — Skill for Claude Code and Codex by termio-sh

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add termio-sh/termio --skill native-app-profiling

Clone the repo

git clone --depth 1 https://github.com/termio-sh/termio

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/termio-sh/termio/native-app-profiling/github.svg)](https://agentmods.dev/skills/termio-sh/termio/native-app-profiling)

Your own site

<a href="https://agentmods.dev/skills/termio-sh/termio/native-app-profiling"><img src="https://agentmods.dev/badge/skills/termio-sh/termio/native-app-profiling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/termio-sh/termio/native-app-profiling"><img src="https://agentmods.dev/badge/skills/termio-sh/termio/native-app-profiling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,229 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00045	$0.01229
Opus 5.5	$0.00018	$0.00492
Sonnet 5.5	$0.00009	$0.00246
Haiku 4.5	$0.00005	$0.00123

Measured 1mo ago against content hash 35e52c4f40a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

native-app-profiling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/native-app-profiling/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Native App Performance Profiling (CLI)

Overview

Record Time Profiler via xctrace, extract samples, symbolicate, and identify hotspots without opening Instruments.

Quick Start

1) Record Time Profiler

Attach to running process:

# Get the PID first
pgrep -x "AppName"

# Record for 90 seconds
xcrun xctrace record \
    --template 'Time Profiler' \
    --time-limit 90s \
    --output /tmp/App.trace \
    --attach <pid>

Launch and record:

xcrun xctrace record \
    --template 'Time Profiler' \
    --time-limit 90s \
    --output /tmp/App.trace \
    --launch -- /path/to/App.app/Contents/MacOS/App

2) Export Time Samples

List available schemas in the trace:

xcrun xctrace export --input /tmp/App.trace --toc

Export time profile data:

xcrun xctrace export \
    --input /tmp/App.trace \
    --xpath '/trace-toc/run/data/table[@schema="time-profile"]' \
    --output /tmp/time-profile.xml

3) Get Load Address for Symbolication

While the app is running, get the __TEXT segment load address:

vmmap <pid> | grep "__TEXT"

Look for the load address (typically starts with 0x1...).

4) Symbolicate Stack Frames

Use atos to symbolicate addresses:

atos -o /path/to/App.app/Contents/MacOS/App -l 0x100000000 <address>

Workflow Notes

Correct binary: Confirm you're profiling the right build (local vs /Applications)
Trigger the slow path: During capture, perform the action that's slow
Capture duration: If stacks are empty, capture longer or avoid idle time
Symbol matching: Binary symbols must match the trace (same build)

Available Templates

List all profiling templates:

xcrun xctrace list templates

Common templates:

Time Profiler - CPU sampling
Allocations - Memory allocations
Leaks - Memory leak detection
System Trace - System-level activity
Animation Hitches - UI performance

Common Commands

Task	Command
List templates	`xcrun xctrace list templates`
List devices	`xcrun xctrace list devices`
Record help	`xcrun xctrace help record`
Export help	`xcrun xctrace help export`
Get PID	`pgrep -x "AppName"`
Get load address	`vmmap <pid> \| grep __TEXT`
Symbolicate	`atos -o <binary> -l <load-addr> <address>`

Read the full file on GitHub · 184 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 184 lines · 45 tokens per session scan A 35e52c4f40a7

Subscribe to this mod's changes

native-app-profiling is a skill published in the GitHub repository termio-sh/termio (548 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 1,229 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Claude Code Debugging Mobile ade agentic ai-agents cli codex

codegraph

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection…

QwenLM/qwen-code · 0 tokens

stuck

Diagnose frozen, stuck, or slow Qwen Code sessions on this machine. Scans for problematic processes, high CPU/memory usage, hung subprocesses, and debug logs. Use /stuck or /stuck to focus on a specific process.

QwenLM/qwen-code · 57 tokens

structured-debugging

Hypothesis-driven debugging methodology for hard bugs. Use this skill whenever you're investigating non-trivial bugs, unexpected behavior, flaky tests, or tracing issues through complex systems. Activate proactively when debugging requires more than a quick glance — especially when the first attempt at a fix didn't…

QwenLM/qwen-code · 85 tokens

native-app-profiling