Reference catalog for visual regression coverage decisions - which Storybook stories or pages get baselines, how to choose breakpoints, when to mask vs adjust threshold, when to add or remove a baseline, and a decision matrix for picking among Percy / Chromatic / Playwright / Storybook test-runner. Use when designing visual coverage for a new project or auditing an existing baseline set.

Configures and runs Vitest - Vite-native unit framework with Jest-compatible API (`expect`, `vi.fn`, `vi.mock`, `vi.spyOn`); reads `vite.config.*` so existing Vite plugins work; supports in-source testing via `if (import.meta.vitest)`, browser-mode UI for headed tests, type-checking via `vitest --typecheck`, native ESM, and coverage via v8 (default) or istanbul providers. Use when the user works with Vite-based projects (Vue, Svelte, Solid, modern React with Vite) or is migrating from Jest on an existing Vite project (not bundler-free Node - use jest-tests for that).

Configures and runs AVA - concurrent-by-default JS/TS test framework with isolated test files (each file runs in its own Node process), no globals (explicit `import test from 'ava'`), async-first API, snapshot support, and TypeScript via `@ava/typescript`. Use when AVA is already the chosen framework and the user wants minimal-API parallel-by-default tests, works with libraries (vs apps) where per-file isolation prevents test interference, or is switching from Mocha for per-file process isolation Mocha cannot provide. For choosing between AVA and Mocha, or for Mocha-specific work, use mocha-tests.

Wraps Java's java.time.Clock + InstantSource dependency-injection pattern for testing time-sensitive code. Covers Clock.fixed(instant, zone), Clock.offset(baseClock, duration), Clock.systemDefaultZone() for production, the InstantSource interface (Java 17+), and the recommended dependency-injection pattern (constructor-inject Clock instead of calling Instant.now() directly). Use when you need to wire the clock-injection pattern (Clock.fixed, Clock.offset, MutableClock, InstantSource, Spring @Bean) into JVM (Java / Kotlin / Scala) production or test code. For pure DST transition reference (skipped or repeated hours, IANA DB, cron-double-fire bug classes) without a clock-injection need, use dst-transition-reference instead.

Author and manage Xray test cases (Jira issues with Test issue type) via the GraphQL + REST APIs - create tests, attach steps, link preconditions, set testType (Manual / Cucumber / Generic), associate with requirements, bulk import via JSON. Covers OAuth client_id/client_secret auth, the GraphQL createTest mutation, the REST /api/v2/import/test/bulk endpoint, and the Cucumber-style scenario authoring path. Use for pre-execution case authoring in Jira-anchored teams using Xray. Distinct from Xray's test-execution / test-run features which post results.

Author and manage Allure TestOps test cases via the REST API - create cases, manage projects + suites, attach scenarios with nested steps, link to Jira / GitHub issues, sync with allure-results from CI runs. Covers Bearer-token auth, /api/rs/testcase CRUD endpoints, nested-step `scenario` shape, and the unique Allure TestOps feature of linking automated results back to manual case definitions. Use for pre-execution case authoring in teams using Allure TestOps as the canonical TCM.

Authors test-data factories using Faker: the Python `faker` library, the `@faker-js/faker` JS port, and the `faker-ruby` gem. Owns the library mechanics end to end: install per language, the provider catalogue (person / internet / location / date / finance / lorem), locale selection and multi-locale mode, and seed-based determinism for reproducible runs. Scope is generating fresh values for tests that start from nothing, not replacing values inside an existing dataset that already holds real records - a production dump in staging goes to pii-masking-pipeline-builder in qa-test-data-privacy (its faker-masking-operators reference), which owns referential integrity and re-identification. Prefer this skill when the codebase already uses the Faker family or when cross-language consistency across Python, JS, and Ruby matters; use synthetic-data-toolkit's mimesis reference only when deeper Python locale coverage is the priority. Use when authoring fixtures or factories that need realistic-looking field values.

Verifies that a masked dataset satisfies k-anonymity, l-diversity, and t-closeness by computing equivalence classes over chosen quasi-identifiers and reporting re-identification risk. Covers quasi-identifier selection heuristics, threshold guidance, pycanon API (k_anonymity / l_diversity / t_closeness / report), ARX Java API and GUI workflow, SmartNoise for differential-privacy comparison, and CI-gate integration. Distinct from pii-masking-pipeline-builder's masking-techniques catalog (which lists masking operators but defers k-anonymity measurement to dedicated tooling) and from presidio-pii-detection (which detects PII spans but offers no equivalence-class analysis). Use when you need to confirm whether a masked dataset meets a stated k, l, or t threshold before promoting it to a non-production environment.

Reads a data-product spec (data PRD, dataset README, lineage doc) and emits a structured data contract - schema (columns + types + nullability + PII flags), freshness SLA, volume bounds, distribution invariants, and ownership. The contract is consumable by data-quality tools such as dbt tests, Great Expectations, or Soda checks as their assertion baseline. Use when scoping a new data product or formalizing assertions on an existing one.