external/antfu-skills/vitest/SKILL.md
Vitest fast unit testing framework powered by Vite with Jest-compatible API. Use when writing tests, mocking, configuring coverage, or working with test filtering and fixtures.
npx skillsauth add seikaikyo/dash-skills vitestInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Vitest is a next-generation testing framework powered by Vite. It provides a Jest-compatible API with native ESM, TypeScript, and JSX support out of the box. Vitest shares the same config, transformers, resolvers, and plugins with your Vite app.
Key Features:
The skill is based on Vitest 5.x (beta), generated at 2026-06-22.
| Topic | Description | Reference | |-------|-------------|-----------| | Configuration | Vitest and Vite config integration, defineConfig usage | core-config | | CLI | Command line interface, commands and options | core-cli | | Test API | test/it function, modifiers like skip, only, concurrent | core-test-api | | Describe API | describe/suite for grouping tests and nested suites | core-describe | | Expect API | Assertions with toBe, toEqual, matchers and asymmetric matchers | core-expect | | Hooks | beforeEach, afterEach, beforeAll, afterAll, aroundEach | core-hooks |
| Topic | Description | Reference | |-------|-------------|-----------| | Mocking | Mock functions, modules, timers, dates with vi utilities | features-mocking | | Snapshots | Snapshot testing with toMatchSnapshot and inline snapshots | features-snapshots | | Coverage | Code coverage with V8 or Istanbul providers | features-coverage | | Test Context | Test fixtures, context.expect, test.extend for custom fixtures | features-context | | Concurrency | Concurrent tests, parallel execution, sharding | features-concurrency | | Filtering | Filter tests by name, file patterns, tags | features-filtering | | Test Tags | Label tests with tags to filter runs and apply shared options | features-test-tags | | Reporters | Built-in reporters, default selection, CI/output config | features-reporters | | Benchmarking | Write benchmarks with the bench fixture (Tinybench) | features-benchmarking |
| Topic | Description | Reference | |-------|-------------|-----------| | Vi Utilities | vi helper: mock, spyOn, fake timers, hoisted, waitFor | advanced-vi | | Environments | Test environments: node, jsdom, happy-dom, custom | advanced-environments | | Type Testing | Type-level testing with expectTypeOf and assertType | advanced-type-testing | | Projects | Multi-project workspaces, different configs per project | advanced-projects |
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
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