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

tests/unit/test_scoring.py

@@ -0,0 +1,54 @@
+"""Unit tests for scoring logic."""
+from __future__ import annotations
+
+from prometheus.domain.entities import EvalResult, Trajectory
+from prometheus.domain.scoring import normalize_score, should_accept
+
+
+def _make_eval(scores: list[float]) -> EvalResult:
+    return EvalResult(
+        scores=scores,
+        feedbacks=[""] * len(scores),
+        trajectories=[
+            Trajectory(f"in{i}", f"out{i}", s, "", "p")
+            for i, s in enumerate(scores)
+        ],
+    )
+
+
+class TestShouldAccept:
+    def test_accepts_improvement(self) -> None:
+        old = _make_eval([0.3, 0.4])
+        new = _make_eval([0.8, 0.9])
+        assert should_accept(old, new) is True
+
+    def test_rejects_regression(self) -> None:
+        old = _make_eval([0.8, 0.9])
+        new = _make_eval([0.3, 0.4])
+        assert should_accept(old, new) is False
+
+    def test_rejects_equal(self) -> None:
+        old = _make_eval([0.5, 0.5])
+        new = _make_eval([0.5, 0.5])
+        assert should_accept(old, new) is False
+
+    def test_min_improvement_threshold(self) -> None:
+        old = _make_eval([0.5])
+        new = _make_eval([0.6])
+        assert should_accept(old, new, min_improvement=0.2) is False
+        assert should_accept(old, new, min_improvement=0.05) is True
+
+
+class TestNormalizeScore:
+    def test_clamps_high(self) -> None:
+        assert normalize_score(1.5) == 1.0
+
+    def test_clamps_low(self) -> None:
+        assert normalize_score(-0.5) == 0.0
+
+    def test_passes_within_range(self) -> None:
+        assert normalize_score(0.7) == 0.7
+
+    def test_custom_range(self) -> None:
+        assert normalize_score(15.0, min_val=0.0, max_val=10.0) == 10.0
+        assert normalize_score(-5.0, min_val=0.0, max_val=10.0) == 0.0