test(translator): add data-driven coverage, bug-exposing cases, and real provider smoke

- matrix.js generates N providers x M models from PROVIDER_MODELS
- coverage-all-models + format-roundtrip for structural/semantic checks
- bugs-* files expose known translation issues via it.fails
- real/smoke-providers runs full handleChatCore path against live providers (RUN_REAL=1), concurrent
- registerAll.js eagerly imports translators (Vitest ESM require fix)
- vitest.config: array aliases for subpaths + maxConcurrency 60

Co-authored-by: Cursor <cursoragent@cursor.com>

tests/translator/coverage-all-models.test.js

@@ -0,0 +1,53 @@
+// Tier 1 — Structural coverage: every model in PROVIDER_MODELS must translate
+// without throwing, correct upstreamId, strip applied. Data-driven → new providers auto-covered.
+import { describe, it, expect } from "vitest";
+import "./registerAll.js";
+import { translateRequest } from "../../open-sse/translator/index.js";
+import { FORMATS } from "../../open-sse/translator/formats.js";
+import { buildProviderGroups, buildModelMatrix, resolveTargetFormat } from "./matrix.js";
+
+// Base OpenAI-format request with text + tool + image (exercises strip + tool paths)
+function baseBody(modelId) {
+  return {
+    model: modelId,
+    stream: true,
+    max_tokens: 64,
+    messages: [
+      { role: "system", content: "You are a helper." },
+      {
+        role: "user",
+        content: [
+          { type: "text", text: "Hello" },
+          { type: "image_url", image_url: { url: "data:image/png;base64,AAAA" } },
+        ],
+      },
+    ],
+    tools: [
+      { type: "function", function: { name: "get_time", description: "x", parameters: { type: "object", properties: {} } } },
+    ],
+  };
+}
+
+const groups = buildProviderGroups();
+
+describe("coverage: every model translates without throwing", () => {
+  it.each(groups)("$alias: all models OpenAI→target", ({ alias, models }) => {
+    for (const m of models) {
+      const target = resolveTargetFormat(alias, m.id);
+      const body = baseBody(m.id);
+      // source = openai (lingua franca); exercise openai → target path
+      const out = translateRequest(FORMATS.OPENAI, target, m.id, body, true, null, alias);
+      expect(out, `${alias}/${m.id} → ${target} returned falsy`).toBeTruthy();
+    }
+  });
+});
+
+const stripModels = buildModelMatrix().filter((r) => r.strip.includes("image"));
+describe.skipIf(stripModels.length === 0)("coverage: image-strip models drop image content", () => {
+  it.each(stripModels)("$alias/$modelId strips image when strip=[image]", (row) => {
+    const body = baseBody(row.modelId);
+    const out = translateRequest(FORMATS.OPENAI, row.targetFormat, row.modelId, body, true, null, row.alias, null, row.strip);
+    const json = JSON.stringify(out);
+    expect(json).not.toContain("data:image/png");
+  });
+});