mobile-use is an open-source AI agent that controls Android and iOS applications from natural-language instructions. It is used to automate phone tasks, navigate app interfaces, and extract structured information from apps. The catalogue skill helps agents operate mobile-use.
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 skills add minitap-ai/mobile-use --skill mobile-use-setupgit clone --depth 1 https://github.com/minitap-ai/mobile-useWrote 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/minitap-ai/mobile-use/mobile-use-setup)<a href="https://agentmods.dev/skills/minitap-ai/mobile-use/mobile-use-setup"><img src="https://agentmods.dev/badge/skills/minitap-ai/mobile-use/mobile-use-setup/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.
<a href="https://agentmods.dev/skills/minitap-ai/mobile-use/mobile-use-setup"><img src="https://agentmods.dev/badge/skills/minitap-ai/mobile-use/mobile-use-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.00560 |
| Opus 5.5 | $0.00017 | $0.00224 |
| Sonnet 5.5 | $0.00008 | $0.00112 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
mobile-use-setup 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mobile-Use SDK Setup
Set up mobile-use so the agent runs locally against a selected device.
Gather Requirements
Ask only for information not already provided:
- Target: iOS, Android, or both.
- Device path: local physical device/emulator, BrowserStack, or a Minitap cloud device.
- LLM provider: Minitap API, OpenAI, Anthropic, Google, OpenRouter, a local model, or another supported provider.
Check Prerequisites
python3 --version # Requires 3.12+
which uv
# Android local
which adb
adb devices
# iOS simulator
which idb_companion
xcrun simctl list devices
# iOS physical
which idevice_id
which appium
appium driver list
For missing dependencies, use the device-specific reference files in this skill.
Create The Project
uv init <project-name>
cd <project-name>
uv add minitap-mobile-use python-dotenv
cp llm-config.override.template.jsonc llm-config.override.jsonc
Configure the selected provider in llm-config.override.jsonc and put only the required provider keys in .env. Add .env to .gitignore.
Create The Starter
Generate task code that runs the agent locally:
import asyncio
from dotenv import load_dotenv
from minitap.mobile_use.sdk import Agent
from minitap.mobile_use.sdk.builders import Builders
from minitap.mobile_use.sdk.types import AgentProfile
load_dotenv()
async def main() -> None:
profile = AgentProfile(name="default", from_file="llm-config.override.jsonc")
config = Builders.AgentConfig.with_default_profile(profile).build()
agent = Agent(config=config)
try:
await agent.init()
result = await agent.run_task(goal="Your automation goal", name="first-task")
print(result)
finally:
await agent.clean()
if __name__ == "__main__":
asyncio.run(main())
When selected, extend config with the BrowserStack or for_cloud_device(...) builder API.
Verify
Verify the selected device is visible, then run:
uv run python -c "from minitap.mobile_use.sdk import Agent; print('SDK OK')"
uv run python main.py
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 96 lines · 42 tokens per session scan A c2c28313aee0
mobile-use-setup is a skill published in the GitHub repository minitap-ai/mobile-use (3,298 stars, last pushed 4d ago), licensed Apache-2.0. It adds 42 tokens to every session and 560 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.
Other skills, from other repositories
agent-device
Automates Apple-platform apps (iOS, tvOS, macOS), Android devices, and Amazon Vega OS TV apps in Vega Virtual Devices. Use when navigating apps, taking snapshots/screenshots where supported, driving TV remotes, tapping, typing, scrolling, extracting UI info, collecting evidence, or planning agent-device CLI commands.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
unbrowse
Search and call websites through Unbrowse's hosted API or remote MCP, reuse indexed site tools, read pages, and learn missing routes in its cloud browser. Use for structured website tasks, and authenticated site access with Unbrowse.
browser-tools
Safety rules over the upstream agent-browser skill (scraping ethics, auth, request pacing, local URLs). URL policy is the Claude Code sandbox's job when the operator enables it. Use for browser automation.
emulate-seed
Generate emulate seed configs for stateful API emulation. Wraps Vercel's emulate tool for GitHub, Vercel, Google OAuth, Slack, Apple Auth, Microsoft Entra, AWS, Okta, Clerk, Resend, Stripe, and MongoDB Atlas APIs — full state machines, not mocks. Use when setting up test environments, CI pipelines, integration tests…