muxinc/ai · Archived

capture-api-response-test-fixture

Capture API response test fixture.

First seen Jun 20, 2026

Installation

$ npx skills add muxinc/ai --skill capture-api-response-test-fixture

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Repository health

Stars 70
License LICENSE
Default branch main
Open issues 13
Status Archived

Package contents

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  • skill md SKILL.md 2,022 B
  • docs SUMMARY.md 75 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

API Response Test Fixtures

For provider response parsing tests, we aim at storing test fixtures with the true responses from the providers (unless they are too large in which case some cutting that does not change semantics is advised).

The fixtures are stored in a fixtures subfolder, e.g. packages/openai/src/responses/fixtures. See the file names in packages/openai/src/responses/fixtures for naming conventions and packages/openai/src/responses/openai-responses-language-model.test.ts for how to set up test helpers.

You can use our examples under /examples/ai-functions to generate test fixtures.

generateText (doGenerate testing)

For generateText, log the raw response output to the console and copy it into a new test fixture.

import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

import { run } from "../lib/run";

run(async () => {
  const result = await generateText({
    model: openai("gpt-5-nano"),
    prompt: "Invent a new holiday and describe its traditions.",
  });

  console.warn(JSON.stringify(result.response.body, null, 2));
});

streamText (doStream testing)

For streamText, you need to set includeRawChunks to true and use the special saveRawChunks helper. Run the script from the /example/ai-functions folder via pnpm tsx src/stream-text/script-name.ts. The result is then stored in the /examples/ai-functions/output folder. You can copy it to your fixtures folder and rename it.

import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

import { run } from "../lib/run";
import { saveRawChunks } from "../lib/save-raw-chunks";

run(async () => {
  const result = streamText({
    model: openai("gpt-5-nano"),
    prompt: "Invent a new holiday and describe its traditions.",
    includeRawChunks: true,
  });

  await saveRawChunks({ result, filename: "openai-gpt-5-nano" });
});