Total 53,723 skills, Testing & QA has 1919 skills
Showing 12 of 1919 skills
Start the dev server, discover API routes from the codebase, hit every endpoint, and report which ones return errors.
cmux testing rules for Swift Testing, test target compilation, and package/refactor validation. Use when adding or changing tests, touching package/refactor code, or deciding whether reload.sh is enough validation.
Generate durable, re-runnable validation scripts from testing intent. Supports TypeScript, Python, Bash with auto-detection, enforces context-optimal PASS/FAIL + /tmp log output, and auto-documents scripts in CLAUDE.md. Triggers on phrases like "turn this into a script", "I want to test/validate X end-to-end", "wrap this in a re-runnable script", or whenever an agent needs durable validation instead of throwaway commands.
Use when writing unit tests for Move smart contracts on Sui. Applies to test function naming, assertions, test attributes, context usage, and cleanup patterns. Use whenever user asks to write tests, add tests, or test a Move module.
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.**
Remap the function_id:pc_index to the original source code position by provided debug info json file.
Use when creating, running, or debugging elastic-package tests — pipeline fixture authoring and expected output, system tests with mock API wiring, and script tests for failure paths and upgrades. Load the reference file for the test type you are working on.
Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/AlloyDB Omni/MySQL/Oracle/MSSQL/CockroachDB/Yugabyte/MongoDB/GizmoSQL/Redis/Spanner/BigQuery/Azurite/MinIO tests. Not for mocked DBs.
QA-test a website or web app and return a 1-5 quality score (5 = flawless, 1 = broken) with evidence. Use when the user wants to test, QA, evaluate, score, or "check how good" a site, page, flow, or app — including a local dev server (e.g. "qa test localhost:5173", "does the checkout work?", "rate this landing page"). Drives a real Browser Use cloud browser, tunneling localhost automatically.
Create repeatable QA regression E2E tests as Argent flows from test cases, tickets, or acceptance criteria. Use when the user asks to generate or preserve an automated regression scenario, with deterministic setup, stable targets, executable structural or visual evidence, and two consecutive full passes. For one-off UI checks or replayable paths without acceptance criteria, use argent-test-ui-flow or argent-create-flow. Supports iOS, Android, Chromium, and Vega (Fire TV), where recorded tv-remote steps replace touch directives. Apple TV and Android TV are unsupported; use argent-tv-interact there and report the limitation.
Comprehensive mobile app testing strategies for iOS and Android. Covers unit tests, UI tests, integration tests, performance testing, and test automation with Detox, Appium, and XCTest.
Refactors brittle snapshot tests into resilient, focused assertions. Provides strategies for reducing snapshot size, extracting meaningful assertions, and maintaining snapshots. Use for "snapshot testing", "snapshot refactoring", "brittle tests", or "assertion improvement".