Mock Vercel AI SDK model calls for deterministic unit and integration tests.
npx skills add https://github.com/jonmumm/skills --skill ai-sdk-testing
Please help me install this Agent Skill. Skill: ai-sdk-testing Source: https://github.com/jonmumm/skills/tree/main/ai-sdk-testing Command: npx skills add https://github.com/jonmumm/skills --skill ai-sdk-testing
This Skill enables writing deterministic, fast tests for code that uses the Vercel AI SDK. LLM calls are slow, nondeterministic, and expensive — never call real providers in tests. Instead, use the SDK's built-in mock providers (ai/test) to control outputs exactly, and assert on the behavior of your code around those outputs.
Use the ai-sdk-testing skill to create a deterministic test for a module that consumes ai.generateText and ai.streamText, asserting outputs without contacting real providers.
High-intent search queries and answers about installing and using this skill.
Write deterministic Vercel AI SDK tests by replacing real LLM calls with mock providers from the ai/test module, ensuring fast and reproducible test execution without network latency or API costs.
Yes, you can mock streaming responses using simulateReadableStream to test streamText code paths deterministically, verifying token delivery and UI streaming endpoint behavior without contacting real LLM providers.
Yes, testing structured output parsing is supported without real API calls by utilizing MockLanguageModelV3 to control and inject exact mock outputs, enabling deterministic assertion validation.
Verify token usage and error handling in Vercel AI SDK tests by configuring mock models to return specific usage metrics and simulated errors, allowing precise assertions on error handling logic.
Use mock models instead of real LLM providers because real LLM calls are slow, nondeterministic, and expensive, while mocks provide exact output control for reliable and fast automated test suites.
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