- Realtime transcription: report malformed provider websocket JSON frames with owned parser errors instead of leaking raw `SyntaxError` objects.

- Microsoft Foundry: report malformed Azure CLI token JSON with owned auth errors instead of leaking raw parser failures.

- Gateway/model pricing: report malformed external pricing catalog JSON with source-owned errors instead of leaking raw parser failures.

- QA Lab: report malformed model-catalog subprocess JSON with an owned error and ignore invalid catalog rows.

- Models config/auth: stop inferring provider env-var markers from broad `^[A-Z_][A-Z0-9_]*$` strings, and resolve config-backed provider `apiKey` values only through structured env SecretRefs (`secrets.providers[id]` / `secrets.defaults`), so unrelated env vars cannot accidentally become provider credentials. Thanks @sallyom.

- Media fetch: skip allocating and buffering the response body for bodyless media responses (HEAD probes and 204-style empty bodies), avoiding wasted heap on streams that carry no payload. Thanks @shakkernerd.

- CLI/onboarding: forward provider-specific auth flags (e.g. `--openai-api-key`) through the onboarding wizard so they reach provider auth methods via `ctx.opts`, letting `--openai-api-key "$OPENAI_API_KEY"` skip the redundant "use existing env var?" prompt in non-interactive harnesses. (#81669) Thanks @sjf.

import { describe, expect, it } from "vitest";

import { selectQaRunnerModelOptions } from "./model-catalog.runtime.js";

import {

parseQaRunnerModelOptionsOutput,

selectQaRunnerModelOptions,

} from "./model-catalog.runtime.js";

describe("qa runner model catalog", () => {

it("filters to available rows and prefers gpt-5.5 first", () => {

]).map((entry) => entry.key),

).toEqual(["openai/gpt-5.5", "anthropic/claude-sonnet-4-6"]);

});

it("reports malformed catalog JSON with an owned error", () => {

expect(() => parseQaRunnerModelOptionsOutput("{not json")).toThrow(

"qa model catalog returned malformed JSON",

);

});

it("ignores invalid catalog rows without failing the model picker", () => {

expect(

parseQaRunnerModelOptionsOutput(

JSON.stringify({

models: [

null,

{

key: "openai/gpt-5.5",

name: "gpt-5.5",

input: "text,image",

available: true,

missing: false,

},

],

}),

).map((entry) => entry.key),

).toEqual(["openai/gpt-5.5"]);

});

});

});

}

function isModelRow(value: unknown): value is ModelRow {

if (!value || typeof value !== "object") {

return false;

}

const row = value as Partial<ModelRow>;

return (

typeof row.key === "string" &&

typeof row.name === "string" &&

typeof row.input === "string" &&

(row.available === true || row.available === false || row.available === null) &&

typeof row.missing === "boolean"

);

}

export function parseQaRunnerModelOptionsOutput(stdout: string): QaRunnerModelOption[] {

let payload: unknown;

try {

payload = JSON.parse(stdout) as unknown;

} catch {

throw new Error("qa model catalog returned malformed JSON");

}

if (!payload || typeof payload !== "object" || Array.isArray(payload)) {

throw new Error("qa model catalog returned invalid JSON payload");

}

const rows = (payload as { models?: unknown }).models;

return selectQaRunnerModelOptions(Array.isArray(rows) ? rows.filter(isModelRow) : []);

}

const CATALOG_ABORT_ERROR_MESSAGE = "qa model catalog aborted";

function createCatalogAbortError() {

});

});

const payload = JSON.parse(Buffer.concat(stdout).toString("utf8")) as { models?: ModelRow[] };

return selectQaRunnerModelOptions(payload.models ?? []);

return parseQaRunnerModelOptionsOutput(Buffer.concat(stdout).toString("utf8"));

} finally {

await fs.rm(tempRoot, { recursive: true, force: true });

}