Total 50,503 skills, Testing & QA has 1780 skills
Showing 12 of 1780 skills
Error tracking and monitoring integration. Sentry, Datadog RUM, Bugsnag. Source maps, breadcrumbs, release tracking, performance monitoring, and alerting configuration. USE WHEN: user mentions "Sentry", "error tracking", "Bugsnag", "Datadog RUM", "crash reporting", "source maps", "release tracking", "error monitoring" DO NOT USE FOR: application logging - use logging skills; APM/tracing - use `opentelemetry`; structured error responses - use `error-handling`
Scientific method expert for systematic bug investigation and root cause analysis. Use when users report bugs, crashes, unexpected behavior, or debugging requests. Applies hypothesis-driven investigation, controlled experiments, and rigorous validation across any programming language or platform.
Kotlin testing patterns with Kotest, MockK, coroutine testing, property-based testing, and Kover coverage. Follows TDD methodology with idiomatic Kotlin practices.
Activate this skill when BenchmarkDotNet (BDN) is involved in the task — creating, running, configuring, or reviewing BDN benchmarks. Also activate when microbenchmarking .NET code would be useful and BenchmarkDotNet is the likely tool. Consider activating when answering a .NET performance question requires measurement and BenchmarkDotNet may be needed. Covers microbenchmark design, BDN configuration and project setup, how to run BDN microbenchmarks efficiently and effectively, and using BDN for side-by-side performance comparisons. Do NOT use for profiling/tracing .NET code (dotnet-trace, PerfView), production telemetry, or load/stress testing (Crank, k6).
Generates production-grade Selenium WebDriver automation scripts and tests in Java, Python, JavaScript, C#, Ruby, or PHP. Supports local execution and TestMu AI cloud with 3000+ browser/OS combinations. Use when the user asks to write Selenium tests, automate with WebDriver, run cross-browser tests on Selenium Grid, or mentions "Selenium", "WebDriver", "RemoteWebDriver", "ChromeDriver", "GeckoDriver". Triggers on: "Selenium", "WebDriver", "browser automation", "Selenium Grid", "cross-browser", "TestMu", "LambdaTest".
Use this skill when testing REST or GraphQL APIs, implementing contract tests, setting up mock servers, or validating API behavior. Triggers on API testing, Postman, contract testing, Pact, mock servers, MSW, HTTP assertions, response validation, and any task requiring API test automation.
Root-cause investigation for bugs, regressions, suspicious runtime behavior, failed automations, and unexpected outcomes.
Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Control and inspect Amazon Fire TV (Vega) apps via argent — launch/restart/reinstall apps, read the on-screen element tree, navigate with the D-pad remote, type, and screenshot. Use when the task mentions Vega, Fire TV, or VVD, or involves driving a Vega virtual device.
Diagnoses and fixes flaky tests by identifying root causes (timing issues, shared state, randomness, network dependencies) and provides stabilization strategies. Use for "flaky tests", "test stability", "intermittent failures", or "test debugging".
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).