Use when writing NetworkRule integration tests in stripe-android — covers testBodyFromFile, inline JSON modification, request matchers, and fixture patterns
npx skills add https://github.com/stripe/stripe-android --skill network-tests
For instrumentation tests that need mocked network responses, use NetworkRule (from network-testing module) with testBodyFromFile. JSON fixture files live in the module's src/androidTest/resources/ directory (e.g., paymentsheet/src/androidTest/resources/checkout-session-init.json) and are resolved by filename from the resources root.
@RunWith(AndroidJUnit4::class)
internal class MyFeatureTest {
@get:Rule
val testRules: TestRules = TestRules.create()
private val networkRule = testRules.networkRule
@Test
fun testSomething() = runPaymentSheetTest(
networkRule = networkRule,
resultCallback = ::assertCompleted,
) { testContext ->
networkRule.enqueue(requestMatcher) { response ->
response.testBodyFromFile("my-fixture.json")
}
// ... test actions ...
}
}
When a test needs a modified JSON response, use the testBodyFromFile lambda to modify the base fixture inline — do NOT create a separate JSON file for each variation.
// GOOD: Modify the base fixture inline
networkRule.checkoutInit { response ->
response.testBodyFromFile("checkout-session-init.json") { json ->
json.put("customer_email", "[email protected]")
}
}
// BAD: Creating checkout-session-init-with-email.json with one field different
networkRule.checkoutInit { response ->
response.testBodyFromFile("checkout-session-init-with-email.json")
}
This keeps the fixture set minimal and makes the test-specific modifications explicit at the call site.
The lambda receives a JSONObject — use standard org.json methods to add or modify fields:
networkRule.checkoutInit { response ->
response.testBodyFromFile("checkout-session-init.json") { json ->
json.put("customer", JSONObject("""
{
"id": "cus_12345",
"payment_methods": [],
"can_detach_payment_method": true
}
""".trimIndent()))
json.put("customer_managed_saved_payment_methods_offer_save", JSONObject("""
{"enabled": true, "status": "not_accepted"}
""".trimIndent()))
}
}
For nested modifications, chain getJSONObject():
response.testBodyFromFile("checkout-session-init.json") { json ->
json.getJSONObject("server_built_elements_session_params")
.getJSONObject("deferred_intent")
.put("setup_future_usage", "off_session")
}
When multiple tests share the same JSON modification, extract the lambda as a parameter:
private fun runMyTest(
jsonModifier: (JSONObject) -> Unit = {},
) = runPaymentSheetTest(networkRule = networkRule, resultCallback = ::assertCompleted) { testContext ->
networkRule.checkoutInit { response ->
response.testBodyFromFile("checkout-session-init.json", jsonModifier)
}
// ... shared test logic ...
}
@Test
fun testWithSfu() = runMyTest { json ->
json.getJSONObject("server_built_elements_session_params")
.getJSONObject("deferred_intent")
.put("setup_future_usage", "off_session")
}
| Signature | Use when |
|-----------|----------|
| testBodyFromFile("file.json") | No modifications needed |
| testBodyFromFile("file.json") { json -> ... } | Modifying JSON fields inline |
| testBodyFromFile("file.json", replacements) | String-level find/replace with ResponseReplacement |
Use RequestMatchers (from com.stripe.android.networktesting.RequestMatchers) to validate request body parameters. Import the matchers you need statically:
import com.stripe.android.networktesting.RequestMatchers.bodyPart
import com.stripe.android.networktesting.RequestMatchers.hasBodyPart
import com.stripe.android.networktesting.RequestMatchers.not
networkRule.checkoutConfirm(
bodyPart("expected_amount", "5099"),
not(hasBodyPart("save_payment_method")),
) { response ->
response.testBodyFromFile("checkout-session-confirm.json")
}
bodyPart(), hasBodyPart(), and query(name, value) auto-decode both the matcher arguments and the request body before comparing. Use plain readable strings — urlEncode() is unnecessary:
// Keys with brackets and values with special characters — just use plain strings
bodyPart("billing_details[email]", "[email protected]")
bodyPart("billing_details[address][line1]", "123 Main St")
bodyPart("payment_method_data[allow_redisplay]", "unspecified")
// urlEncode() still works (backward compatible) but is unnecessary
// bodyPart(urlEncode("billing_details[email]"), urlEncode("[email protected]"))
When a request doesn't match a mock, the error message shows per-matcher diagnostics with the nearest-miss mock:
POST https://localhost/v1/payment_intents/pi_123/confirm
Body params: {billing_details[email][email protected], payment_method=pm_123}
Nearest mock: composite(path(/v1/confirm), bodyPart(billing_details[email], [email protected]))
+ PASS: path(/v1/confirm)
+ PASS: method(POST)
- FAIL: bodyPart(billing_details[email], [email protected])
See PaymentSheetBillingConfigurationTest.kt for more examples.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take stripe/network-tests from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.