Add judge-prompt cache bootstrap script

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

tests/test_judge_prompt_cache_builder.py

@@ -0,0 +1,80 @@
+"""Tests for the prompt-cache bootstrap script (mocked adapt/LLM)."""
+
+import asyncio
+import json
+
+from pydantic import BaseModel
+
+import scripts.build_judge_prompt_cache as builder
+
+
+class _In(BaseModel):
+    question: str
+
+
+class _Out(BaseModel):
+    statements: list[str]
+
+
+class _FakeAdapted:
+    """Fake adapted prompt returned by prompt.adapt()."""
+
+    def __init__(self):
+        self.instruction = "中文指令。"
+        self.language = "chinese"
+        self.examples = [(_In(question="问题"), _Out(statements=["陈述"]))]
+
+
+class _FakePrompt:
+    """Fake prompt with async adapt() that returns _FakeAdapted."""
+
+    def __init__(self):
+        self.input_model = _In
+        self.output_model = _Out
+        self.instruction = "English."
+        self.examples = [(_In(question="q"), _Out(statements=["s"]))]
+        self.language = "english"
+
+    async def adapt(self, target_language, llm, adapt_instruction=False):
+        assert target_language == "chinese"
+        assert adapt_instruction is True
+        return _FakeAdapted()
+
+
+def test_serialize_prompt_shape():
+    """serialize_prompt emits the expected committed cache schema."""
+    data = builder.serialize_prompt(
+        _FakeAdapted(), "hash123", "context_recall", "prompt", "zh", "0.4.3"
+    )
+    assert data["metric"] == "context_recall"
+    assert data["prompt_attr"] == "prompt"
+    assert data["language"] == "chinese"
+    assert data["source_hash"] == "hash123"
+    assert data["instruction"] == "中文指令。"
+    assert data["examples"] == [
+        {"input": {"question": "问题"}, "output": {"statements": ["陈述"]}}
+    ]
+
+
+def test_build_cache_writes_all_files(tmp_path, monkeypatch):
+    """build_cache writes one JSON per (metric, attr) under CACHE_ROOT/<language>/."""
+    # Build a fake registry where every prompt attr is a _FakePrompt.
+    fake_registry = {name: type("M", (), {})() for name in builder.METRIC_PROMPT_ATTRS}
+    for name, attrs in builder.METRIC_PROMPT_ATTRS.items():
+        for attr in attrs:
+            setattr(fake_registry[name], attr, _FakePrompt())
+
+    monkeypatch.setattr(builder, "build_models", lambda j, e, s: (object(), object()))
+    monkeypatch.setattr(builder, "build_metric_registry", lambda llm, emb: fake_registry)
+    monkeypatch.setattr(builder, "CACHE_ROOT", tmp_path)
+
+    written = asyncio.run(builder.build_cache("zh", "gpt-5", "emb", object()))
+
+    expected_count = sum(len(a) for a in builder.METRIC_PROMPT_ATTRS.values())
+    assert len(written) == expected_count
+
+    sample = tmp_path / "zh" / "context_recall__prompt.json"
+    assert sample.exists()
+    data = json.loads(sample.read_text(encoding="utf-8"))
+    assert data["instruction"] == "中文指令。"
+    assert data["metric"] == "context_recall"