회기 무발화 0턴 분리, 자기예측 락 불변식 및 TDD 회귀 검증 완료

apps/api/app/services/calibration_transfer_store.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import hashlib
 import json
 from collections.abc import Mapping, Sequence
 from datetime import UTC, datetime
@@ -26,6 +25,13 @@ from .calibration_transfer import (
     assess_synthetic_subgroup_drift,
     assess_transfer,
 )
+from .outcome_repository_values import (
+    canonical_hash as _canonical_hash,
+    created_role as _created_role,
+    public_row as _public_row,
+    value as _value,
+)
+from .practice_competency import target_techniques
 
 
 class CalibrationTransferNotFoundError(LookupError):
@@ -64,36 +70,6 @@ _POSITIVE_CLIENT_STATES = frozenset(
 )
 
 
-def _value(row: Mapping[str, Any], key: str, default: Any = None) -> Any:
-    try:
-        return row[key]
-    except (KeyError, TypeError):
-        return default
-
-
-def _public_row(row: Mapping[str, Any]) -> dict[str, Any]:
-    """API 응답에서 학습자 피드백 정책 판정 전용 열을 제거한다."""
-
-    payload = dict(row)
-    payload.pop("source_learner_feedback_enabled", None)
-    return payload
-
-
-def _canonical_hash(payload: Mapping[str, Any]) -> str:
-    serialized = json.dumps(
-        payload,
-        ensure_ascii=False,
-        separators=(",", ":"),
-        sort_keys=True,
-        default=str,
-    )
-    return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
-
-
-def _created_role(principal: Principal) -> str:
-    return "instructor" if principal.role == Role.TEACHER else principal.role.value
-
-
 def _ensure_unique_evidence(
     evidence_turn_ids: Sequence[UUID], *, required: bool = False
 ) -> tuple[UUID, ...]:
@@ -182,6 +158,16 @@ async def append_prediction_revision(
     reason = revision_reason.strip()
     if not reason:
         raise CalibrationTransferStateError("revision_reason must not be blank")
+    if not (0.0 <= predicted_success_probability <= 1.0):
+        raise CalibrationTransferStateError(
+            "predicted_success_probability must be between 0.0 and 1.0"
+        )
+    if not (0.0 <= confidence <= 1.0):
+        raise CalibrationTransferStateError(
+            "confidence must be between 0.0 and 1.0"
+        )
+    if instrument_id == "vignette.calibration-self-prediction":
+        instrument_id = "calibration-mirror-g5"
     payload = {
         "prediction_revision_id": str(prediction_revision_id),
         "history_id": str(history_id),
@@ -318,6 +304,11 @@ async def append_prediction_revision(
             asyncpg.ForeignKeyViolationError,
             asyncpg.ObjectNotInPrerequisiteStateError,
         ) as exc:
+            message = str(exc)
+            if "lock" in message.lower() or "reveal" in message.lower():
+                raise CalibrationTransferStateError(
+                    f"self-prediction history is locked or revealed: {message}"
+                ) from exc
             raise CalibrationTransferStateError(
                 "prediction revision violated provenance or history invariants"
             ) from exc
@@ -880,21 +871,9 @@ async def append_transfer_suite(
 
 
 def _actual_target_techniques(competency_id: str) -> frozenset[str]:
-    key = competency_id.lower()
-    if any(token in key for token in ("empathy", "empathic", "reflection")):
-        return frozenset({"empathy", "reflection", "validation", "restatement"})
-    if any(token in key for token in ("open_question", "open-question")):
-        return frozenset({"facilitative_question", "exploration", "clarification"})
-    if any(token in key for token in ("rupture", "repair", "impact")):
-        return frozenset(
-            {"opinion_check", "validation", "reflection", "here_and_now_focus"}
-        )
-    if any(token in key for token in ("goal", "collaborative", "reagreement")):
-        return frozenset(
-            {"consent_motivation_check", "opinion_check", "restatement"}
-        )
-    if any(token in key for token in ("presence", "response-space")):
-        return frozenset({"holding", "reflection", "here_and_now_focus"})
+    targets = target_techniques(competency_id)
+    if targets is not None:
+        return targets
     raise CalibrationTransferStateError(
         f"unsupported actual transfer competency: {competency_id}"
     )