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/application/use_cases.py

@@ -0,0 +1,77 @@
+"""
+Main use case — high-level orchestration.
+
+Entry point for business logic. Coordinates bootstrap → evolution → result.
+Contains no technical logic, only orchestration.
+"""
+from __future__ import annotations
+
+from prometheus.application.bootstrap import SyntheticBootstrap
+from prometheus.application.dto import OptimizationConfig, OptimizationResult
+from prometheus.application.evaluator import PromptEvaluator
+from prometheus.application.evolution import EvolutionLoop
+from prometheus.domain.entities import Prompt
+from prometheus.domain.ports import ProposerPort
+
+
+class OptimizePromptUseCase:
+    """Single MVP use case.
+
+    Injects dependencies via constructor (dependency injection).
+    """
+
+    def __init__(
+        self,
+        evaluator: PromptEvaluator,
+        proposer: ProposerPort,
+        bootstrap: SyntheticBootstrap,
+    ):
+        self._evaluator = evaluator
+        self._proposer = proposer
+        self._bootstrap = bootstrap
+
+    def execute(self, config: OptimizationConfig) -> OptimizationResult:
+        """Full pipeline:
+        1. Bootstrap → generate synthetic inputs
+        2. Evolution → optimization loop
+        3. Return result
+        """
+        # Phase 0: Bootstrap
+        synthetic_pool = self._bootstrap.run(
+            task_description=config.task_description,
+            n_examples=config.n_synthetic_inputs,
+        )
+
+        # Phase 1: Evolution
+        loop = EvolutionLoop(
+            evaluator=self._evaluator,
+            proposer=self._proposer,
+            bootstrap=self._bootstrap,
+            max_iterations=config.max_iterations,
+            minibatch_size=config.minibatch_size,
+            perfect_score=config.perfect_score,
+            verbose=config.verbose,
+        )
+        seed_prompt = Prompt(text=config.seed_prompt)
+        state = loop.run(seed_prompt, synthetic_pool, config.task_description)
+
+        # Phase 2: Result
+        initial_score = (
+            state.candidates[0].best_score if state.candidates else 0.0
+        )
+        final_score = state.best_candidate.best_score if state.best_candidate else 0.0
+
+        return OptimizationResult(
+            optimized_prompt=(
+                state.best_candidate.prompt.text
+                if state.best_candidate
+                else config.seed_prompt
+            ),
+            initial_prompt=config.seed_prompt,
+            iterations_used=state.iteration,
+            total_llm_calls=state.total_llm_calls + 1,  # +1 for bootstrap
+            initial_score=initial_score,
+            final_score=final_score,
+            improvement=final_score - initial_score,
+            history=state.history,
+        )