Argent is a toolkit that lets an AI assistant control and inspect iOS, Android, TV, Electron, and Chromium applications through simulators, emulators, physical devices, or desktop connections. It is for developers who want an agent to interact with interfaces, reproduce issues, test features, debug applications, or run profiling tasks. The catalogue add-ons expose Argent's device-control and development workflows to coding agents.
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.
npx agentmods add skills/software-mansion/argent/argent-native-profilernpx skills add software-mansion/argent --skill argent-native-profilergit clone --depth 1 https://github.com/software-mansion/argentWrote 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.
[](https://agentmods.dev/skills/software-mansion/argent/argent-native-profiler)<a href="https://agentmods.dev/skills/software-mansion/argent/argent-native-profiler"><img src="https://agentmods.dev/badge/skills/software-mansion/argent/argent-native-profiler.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.01788 |
| Opus 5 | $0.00018 | $0.00894 |
| Sonnet 5 | $0.00007 | $0.00358 |
| Haiku 4.5 | $0.00004 | $0.00179 |
Grade A, and why
argent-native-profiler 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1. Tools
native-profiler-start— start profiling on a booted device. iOS: xctrace recording for CPU, hangs, and leaks.native-profiler-stop— stop the profiler and export trace data to timestamped XML files.native-profiler-analyze— parse exported trace data and return a structured bottleneck payload.profiler-stack-query— drill into parsed data: hang stacks, function callers, thread breakdown, leak details.profiler-load— list and reload previous trace sessions from disk for re-investigation.- Physical iPhone: not supported; use a simulator.
2. Platform Support
- iOS: Backend: Xcode Instruments via
xctraceon a booted simulator or connected device. Requires Xcode command-line tools on PATH. Surfaces CPU hotspots, UI hangs, and memory leaks (instrumentsLeakstable). - Android: Backend: Perfetto via
adb shell perfetto+ an in-process WASM trace-processor engine. Surfaces CPU hotspots and UI hangs, with per-hang jank reason codes, a main-thread state breakdown withblocked_functionattribution, and a GC overlap annotation. Also reports an RSS-growth signal for memory pressure; treat it as a hint to confirm manually, not a confirmed leak. The target app must be debuggable or include<profileable android:shell="true"/>in its manifest forperf_samplecallstacks to be captured.
3. Investigation Patterns
After native-profiler-analyze surfaces findings, use profiler-stack-query to drill into root causes:
- Hang detected →
profiler-stack-querymode=hang_stacksfor full native call chains → mode=function_callersfor the suspected function → read native source. - CPU hotspot →
profiler-stack-querymode=thread_breakdownfor per-thread distribution → mode=function_callersfor the dominant function. - Memory leak →
profiler-stack-querymode=leak_stacksfiltered byobject_typefor responsible frames and libraries.- iOS: if leaks come back unattributed (responsible frame
<Call stack limit reached>), re-runnative-profiler-startwithmalloc_stack_logging: true. This cold-launches the app with Malloc Stack Logging so leaks carry a real allocation backtrace (responsible frame + library). It restarts the app and adds overhead, so use it only when you need leak attribution — not for CPU/hang passes.
- iOS: if leaks come back unattributed (responsible frame
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.
- yesterday Changed · +1 lines d1a52888c3f0
- 5d ago First seen · 108 lines · 37 tokens per session scan A fc58f51fa626
argent-native-profiler is a skill published in the GitHub repository software-mansion/argent (2,378 stars, last pushed today), licensed Apache-2.0. It adds 37 tokens to every session and 1,788 once invoked, about $0.0002 per session on Opus 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.
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