core: Registry, Skills, Router, Executor, Verifier, Runner mit Tests

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

core/src/knaster/cases.py

@@ -0,0 +1,44 @@
+"""Fälle und Präferenzpaare: modellneutral speichern, deterministisch splitten, exportieren."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from collections.abc import Iterator
+from typing import Any
+
+from .store import Store
+
+EVAL_SHARE_PERCENT = 20
+
+
+def split_for(text: str) -> str:
+    """Fester Split pro Eingabe: gleiche Eingabe landet immer im selben Split."""
+    bucket = int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 100
+    return "eval" if bucket < EVAL_SHARE_PERCENT else "train"
+
+
+def export_sft(store: Store, *, cluster: str, split: str = "train") -> Iterator[str]:
+    """JSONL für Supervised Finetuning: nur unabhängig verifizierte Fälle."""
+    for case in reversed(store.list_cases(cluster=cluster, split=split, passed=True)):
+        messages = list(case["messages"] or [])
+        messages.append({"role": "assistant", "content": case["output"]})
+        meta = {k: case[k] for k in ("id", "skill", "skill_version", "stage", "model", "origin")}
+        yield json.dumps({"messages": messages, "meta": meta}, ensure_ascii=False)
+
+
+def export_preferences(store: Store, *, cluster: str, split: str = "train") -> Iterator[str]:
+    for pair in store.list_preference_pairs(cluster=cluster):
+        if pair["split"] != split:
+            continue
+        yield json.dumps(
+            {"prompt": pair["prompt"], "chosen": pair["chosen"], "rejected": pair["rejected"]},
+            ensure_ascii=False,
+        )
+
+
+def pass_rate(rows: list[dict[str, Any]]) -> float | None:
+    decided = [r for r in rows if r.get("passed") is not None]
+    if not decided:
+        return None
+    return sum(1 for r in decided if r["passed"]) / len(decided)