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 LambdaTest/agent-skills --skill espresso-skillgit clone --depth 1 https://github.com/LambdaTest/agent-skillsWrote 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/lambdatest/agent-skills/espresso-skill)<a href="https://agentmods.dev/skills/lambdatest/agent-skills/espresso-skill"><img src="https://agentmods.dev/badge/skills/lambdatest/agent-skills/espresso-skill/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/lambdatest/agent-skills/espresso-skill"><img src="https://agentmods.dev/badge/skills/lambdatest/agent-skills/espresso-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 185 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00096 | $0.01968 |
| Opus 5 | $0.00048 | $0.00984 |
| Sonnet 5 | $0.00019 | $0.00394 |
| Haiku 4.5 | $0.00010 | $0.00197 |
Grade A, and why
espresso-skill scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -u "$LT_USERNAME:$LT_ACCESS_KEY" \ How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Espresso Automation Skill
You are a senior Android QA engineer specializing in Espresso UI testing.
Step 1 — Execution Target
├─ Mentions "cloud", "TestMu", "LambdaTest", "device farm"?
│ └─ TestMu AI cloud (upload APK + test APK)
│
├─ Mentions "emulator", "local", "connected device"?
│ └─ Local: ./gradlew connectedAndroidTest
│
└─ Default → Local emulator
Core Patterns — Kotlin (Default)
Basic Test
@RunWith(AndroidJUnit4::class)
class LoginTest {
@get:Rule
val activityRule = ActivityScenarioRule(LoginActivity::class.java)
@Test
fun loginWithValidCredentials() {
// Type email
onView(withId(R.id.emailInput))
.perform(typeText("[email protected]"), closeSoftKeyboard())
// Type password
onView(withId(R.id.passwordInput))
.perform(typeText("password123"), closeSoftKeyboard())
// Click login button
onView(withId(R.id.loginButton))
.perform(click())
// Verify dashboard is displayed
onView(withId(R.id.dashboardTitle))
.check(matches(isDisplayed()))
.check(matches(withText("Welcome")))
}
@Test
fun loginWithInvalidCredentials_showsError() {
onView(withId(R.id.emailInput))
.perform(typeText("[email protected]"), closeSoftKeyboard())
onView(withId(R.id.passwordInput))
.perform(typeText("wrong"), closeSoftKeyboard())
onView(withId(R.id.loginButton))
.perform(click())
onView(withId(R.id.errorText))
.check(matches(isDisplayed()))
.check(matches(withText(containsString("Invalid"))))
}
}
ViewMatchers (Finding Elements)
// By ID (best)
onView(withId(R.id.loginButton))
// By text
onView(withText("Login"))
// By content description (accessibility)
onView(withContentDescription("Submit form"))
// By hint text
onView(withHint("Enter your email"))
// Combined matchers
onView(allOf(withId(R.id.button), withText("Submit"), isDisplayed()))
// In RecyclerView
onView(withId(R.id.recyclerView))
.perform(RecyclerViewActions.actionOnItemAtPosition<ViewHolder>(0, click()))
// By parent
onView(allOf(withText("Delete"), isDescendantOfA(withId(R.id.toolbar))))
What ships with it
3 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.
- 10d ago First seen · 259 lines · 96 tokens per session scan A 460208377da2
espresso-skill is a skill published in the GitHub repository LambdaTest/agent-skills (366 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 1,968 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
solopi-ai
A command-line framework for testing Android apps and devices with SoloPi, including on-device or cloud AI decision models. It manages devices, test cases, recorded interactions, replays, performance history, and evidence.
mobile-tester
You are the Mobile Tester Specialist. You connect to real Android/iOS devices and write, execute, and report on mobile UI test cases. You master THREE testing modalities.
device-farms
Expert guidance on running mobile tests on Firebase Test Lab, AWS Device Farm, BrowserStack App Automate, and Sauce Labs. Use when asked to set up device-farm coverage, design a device matrix, or compare vendors.
maestro-flows
Expert guidance on writing, running, and parallelizing Maestro YAML flows for cross-platform mobile E2E testing. Use when adding Maestro coverage, wiring Maestro to CI, or moving off Detox/Appium for smoke flows.
ui-testing-patterns
Compare and apply UI test tooling across mobile — Espresso, XCUITest, fluttertest / integrationtest, Detox, and Maestro. Use when deciding which UI test layer fits a feature and how to structure selectors, waits, and page objects.
android-ui-journey-testing
XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.