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 irahardianto/awesome-agv --skill mobile-testinggit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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/irahardianto/awesome-agv/mobile-testing)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/mobile-testing"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/mobile-testing/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/irahardianto/awesome-agv/mobile-testing"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/mobile-testing.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 | $0.00040 | $0.01568 |
| Opus 5.5 | $0.00016 | $0.00627 |
| Sonnet 5.5 | $0.00008 | $0.00314 |
| Haiku 4.5 | $0.00004 | $0.00157 |
Grade A, and why
mobile-testing 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mobile Testing
Overview
Load this skill when writing E2E or integration tests for mobile applications (Flutter, React Native, or native iOS/Android). This skill complements the browser-automation skill which covers web — this skill covers native mobile.
Framework references: This file covers patterns and strategies. For framework-specific setup, API, and code examples, see:
Screen Object Model
The mobile equivalent of Page Object Model. Encapsulates screen-specific finders, interactions, and assertions in reusable classes.
Principles
- One class per screen — mirrors the app's navigation structure
- Encapsulate finders — expose semantic methods, not raw selectors
- Compose flows — tests chain screen objects into user journeys
- Assert at the screen level — each screen knows its own success/error states
Structure
integration_test/
screens/
login_screen.dart # Finders + interactions for login
home_screen.dart # Finders + interactions for home
checkout_screen.dart # Finders + interactions for checkout
flows/
purchase_flow_test.dart # Composes screen objects into journey
Pattern (language-agnostic)
Each screen object exposes:
- Finders — locators for key UI elements (by key, type, text, accessibility ID)
- Interactions — methods for user actions (
enterEmail(),tapLogin(),scrollToItem()) - Composed actions — higher-level methods combining multiple interactions (
loginAs(email, password)) - Assertions — methods to verify screen state (
assertErrorVisible(),assertWelcomeShown())
For framework-specific implementations, see the references:
- Flutter/Patrol: See flutter.md § Screen Object Model
- Maestro: Uses YAML sub-flows rather than code classes — see maestro.md
What ships with it
2 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 · 137 lines · 40 tokens per session scan A 76e8d853a5bd
mobile-testing is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 9d ago), licensed MIT. It adds 40 tokens to every session and 1,568 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
quality-engineering-appium-mcp
Drives iOS/Android mobile devices via Appium MCP. Runs a preflight, picks local emulator/simulator/USB or a device cloud, captures screenshots/page source into a named evidence dir. Use for verifying mobile bugs, E2E tests, and navigating real device clouds (LambdaTest/BrowserStack).
verify-bug
Post-merge UAT verification workflow. Walks JIRA reproduce steps, performs comparative audits (Before/After), attaches evidence to JIRA, and transitions status on PASS.
test-loop
Plan, generate, and heal an executable E2E test suite from approved acceptance criteria (web and mobile).
verify-work
Verify feature, bug, UI, API, mobile, security, or deployment work against acceptance criteria.
uat-signoff
Walk a business/UAT approver through a plain-language demo of a verified feature and capture business acceptance signoff.
zephyr-coverage-analysis
Analyze Zephyr test case coverage for a Jira user story and produce a QE management report with metrics, risk scoring, and prioritized recommendations.