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/proposer_adapter.py

@@ -0,0 +1,47 @@
+"""
+Adapter: Reflective Mutation Proposer.
+
+Implements the ProposerPort via the DSPy InstructionProposer.
+Converts trajectories into readable format for the LLM proposer.
+"""
+from __future__ import annotations
+
+from prometheus.domain.entities import Prompt, Trajectory
+from prometheus.domain.ports import ProposerPort
+from prometheus.infrastructure.dspy_modules import InstructionProposer
+
+
+class DSPyProposerAdapter(ProposerPort):
+    """Uses evaluation trajectories to build a failure report and propose a new prompt."""
+
+    def __init__(self) -> None:
+        self._proposer = InstructionProposer()
+
+    def propose(
+        self,
+        current_prompt: Prompt,
+        trajectories: list[Trajectory],
+        task_description: str,
+    ) -> Prompt:
+        failure_examples = self._format_failures(trajectories)
+        pred = self._proposer(
+            current_instruction=current_prompt.text,
+            task_description=task_description,
+            failure_examples=failure_examples,
+        )
+        return Prompt(text=pred.new_instruction)
+
+    @staticmethod
+    def _format_failures(trajectories: list[Trajectory]) -> str:
+        """Convert trajectories into a structured textual report."""
+        sections: list[str] = []
+        for i, t in enumerate(trajectories, 1):
+            section = (
+                f"# Example {i}\n"
+                f"## Input\n{t.input_text}\n\n"
+                f"## Generated Output\n{t.output_text}\n\n"
+                f"## Score\n{t.score:.2f}\n\n"
+                f"## Feedback\n{t.feedback}\n"
+            )
+            sections.append(section)
+        return "\n---\n".join(sections)