Initial commit: PROMETHEUS v0.1.0 - Prompt optimizer

- Clean architecture (domain/application/infrastructure)
- DSPy-based evolution engine with scoring
- CLI via pyproject.toml entry point
- Unit + integration tests (~300 tests)
- Configs for glm-5.1 and glm-4.5-air models
- Z.AI endpoint integration

src/prometheus/infrastructure/judge_adapter.py

@@ -0,0 +1,34 @@
+"""
+Adapter: LLM-as-Judge.
+
+Implements the JudgePort via the DSPy OutputJudge module.
+"""
+from __future__ import annotations
+
+from prometheus.domain.ports import JudgePort
+from prometheus.infrastructure.dspy_modules import OutputJudge
+
+
+class DSPyJudgeAdapter(JudgePort):
+    """Evaluates a batch of (input, output) pairs by calling the Judge for each.
+
+    Sequential for MVP. Future: parallelize via dspy.Parallel.
+    """
+
+    def __init__(self) -> None:
+        self._judge = OutputJudge()
+
+    def judge_batch(
+        self,
+        task_description: str,
+        pairs: list[tuple[str, str]],
+    ) -> list[tuple[float, str]]:
+        results: list[tuple[float, str]] = []
+        for input_text, output_text in pairs:
+            pred = self._judge(
+                task_description=task_description,
+                input_text=input_text,
+                output_text=output_text,
+            )
+            results.append((pred.score, pred.feedback))
+        return results