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/bootstrap.py

@@ -0,0 +1,42 @@
+"""
+Bootstrap — synthetic input generation.
+
+Creates a pool of test inputs from the task description.
+This replaces the need for a labelled dataset.
+"""
+from __future__ import annotations
+
+import random
+
+from prometheus.domain.entities import SyntheticExample
+from prometheus.domain.ports import SyntheticGeneratorPort
+
+
+class SyntheticBootstrap:
+    """Orchestrates synthetic input generation.
+
+    Depends only on the abstract port, not on DSPy directly.
+    """
+
+    def __init__(self, generator: SyntheticGeneratorPort, seed: int = 42):
+        self._generator = generator
+        self._rng = random.Random(seed)
+
+    def run(self, task_description: str, n_examples: int) -> list[SyntheticExample]:
+        """Generate the synthetic pool in a single call.
+
+        Single call minimizes LLM cost (1 call instead of N),
+        and the LLM can ensure diversity in a single generation.
+        """
+        examples = self._generator.generate_inputs(task_description, n_examples)
+        self._rng.shuffle(examples)
+        return examples
+
+    def sample_minibatch(
+        self,
+        pool: list[SyntheticExample],
+        size: int,
+    ) -> list[SyntheticExample]:
+        """Sample a minibatch from the synthetic pool."""
+        size = min(size, len(pool))
+        return self._rng.sample(pool, size)