Total 53,169 skills, Testing & QA has 1907 skills
Showing 12 of 1907 skills
Generates .http files for the VS Code REST Client extension from Express, Next.js, Fastify, or other API routes. Creates organized request files with variables, environments, and authentication. Use when users request "generate http files", "rest client requests", "create .http file", or "vscode api testing".
Start the dev server, discover API routes from the codebase, hit every endpoint, and report which ones return errors.
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.
Harden the test surface after `/cook` — map changed behavior to tests, find weak assertions and missing boundaries, and add focused hardening tests. Use when the user wants the tests strengthened before review or shipping — phrases like "press the changes", "harden this", "check coverage", "strengthen the tests", "are the tests good enough", "press before /age", "/press". Use even when the user wants to "tighten things up" before review. Do NOT use to add broad new behavior — only corrective fixes that hardening tests force.
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.
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.
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.
Scope coverage, build a testing strategy, test the feature with agent-browser, capture screenshots/video (always mandatory), upload to S3, and create a structured test report (never skip)
Set up Python test environment in Claude Code for web where flox is unavailable. Use when you need to run backend tests and `uv sync` fails due to Python version mismatch.
Generate and run comprehensive test suites — unit tests, integration tests with real services (testcontainers/docker-compose), and Playwright E2E tests. Analyzes coverage gaps, spawns parallel test-generator agents per tier, runs tests, and heals failures (max 3 iterations). Use when generating tests for existing code, improving coverage after implementation, or creating a full test suite from scratch. Chains naturally after /ork:implement. Do NOT use for verifying/grading existing tests (use /ork:verify) or running tests without generation (use npm test directly).