anivar

Zod v4 best practices, patterns, and API guidance for schema validation, parsing, error handling, and type inference in TypeScript applications. Covers safeParse, object composition, refinements, transforms, codecs, branded types, and v3→v4 migration. Baseline: zod ^4.0.0. Triggers on: zod imports, z.object, z.string, z.infer, safeParse, mentions of "zod", "schema validation", "zod v4", or "z.enum".

Enterprise technical documentation best practices, patterns, and frameworks for developer and partner adoption. Covers content architecture (Diataxis four quadrants), 14 content types (tutorials, how-to guides, API reference, SDK docs, migration guides, changelogs, runbooks, integration guides, troubleshooting, architecture docs), pluggable writing styles (Diataxis, Google, Microsoft, Stripe, Canonical, Minimal), information architecture, docs-as-code workflows, documentation audit, anti-patterns checklist, and developer experience (DX) strategy. 27 rules, 5 references, 6 style guides. Baseline: Diataxis + Google OpenDocs + Good Docs Project. Triggers on: "write docs", "document this", "API docs", "developer docs", "migration guide", "changelog", "tutorial", "how-to guide", "reference docs", "documentation strategy", "docs audit", "information architecture", "developer experience", "partner docs", "SDK documentation", "runbook", "troubleshooting guide", "integration guide", "quickstart", "getting started", "t

Deep-dive code analysis with periodic saving. Contributor mode reads every commit diff for annual reviews, accuracy rates, and promotion readiness. Codebase mode maps repository structure, cross-repo relationships, and enterprise governance. Works with GitHub (gh) and GitLab (glab). Saves checkpoints incrementally for resume across sessions.

Jest best practices, patterns, and API guidance for JavaScript/TypeScript testing. Covers mock design, async testing, matchers, timer mocks, snapshots, module mocking, configuration, and CI optimization. Baseline: jest ^29.0.0 / ^30.0.0. Triggers on: jest imports, describe, it, test, expect, jest.fn, jest.mock, jest.spyOn, mentions of "jest", "unit test", "test suite", or "mock".

Write tests for Redux Sagas using redux-saga-test-plan, runSaga, and manual generator testing. Covers expectSaga (integration), testSaga (unit), providers, partial matchers, reducer integration, error simulation, and cancellation testing. Works with Jest and Vitest. Triggers on: test files for sagas, redux-saga-test-plan imports, mentions of "test saga", "saga test", "expectSaga", "testSaga", or "redux-saga-test-plan".

Redux-Saga best practices, patterns, and API guidance for building, testing, and debugging generator-based side-effect middleware in Redux applications. Covers effect creators, fork model, channels, testing with redux-saga-test-plan, concurrency, cancellation, and modern Redux Toolkit integration. Baseline: redux-saga 1.4.2. Triggers on: saga files, redux-saga imports, generator-based middleware, mentions of "saga", "takeEvery", "takeLatest", "fork model", or "channels".

Testing patterns for Zod schemas using Jest and Vitest. Covers schema correctness testing, mock data generation, error assertion patterns, integration testing with API handlers and forms, snapshot testing with z.toJSONSchema(), and property-based testing. Baseline: zod ^4.0.0. Triggers on: test files for Zod schemas, zod-schema-faker imports, mentions of "test schema", "schema test", "zod mock", "zod test", or schema testing patterns.

MSW (Mock Service Worker) v2 best practices, patterns, and API guidance for API mocking in JavaScript/TypeScript tests and development. Covers handler design, server setup, response construction, testing patterns, GraphQL, and v1-to-v2 migration. Baseline: msw ^2.0.0. Triggers on: msw imports, http.get, http.post, HttpResponse, setupServer, setupWorker, graphql.query, mentions of "msw", "mock service worker", "api mocking", or "msw v2".

Evaluate digital and AI infrastructure against the five corrigibility tests (EXIT, CODE, AUDIT, GOVERN, FORK). Use when assessing DPI systems (identity, payments, registries) or EPI systems (AI/ML in public services) for structural corrigibility.