fix(rectification): score family events and allow appearance follow-up

Dated family evidence now moves candidates via D12 and kin houses, career receipts expose both D1-10 and D10, and appearance/marks may be asked as auxiliary first-house scores. New cases bind Skill 10.0.4.

Co-authored-by: Cursor <cursoragent@cursor.com>

scripts/active_rectification_event_engine.py

@@ -53,7 +53,15 @@ DOMAIN_CONFIG: Final[dict[EventDomain, DomainConfig]] = {
     "career": (("D10",), (10,)),
     "finance": (("D2", "D11"), (2, 11)),
     "health_pressure": (("D30",), (6, 8, 12)),
+    # 六亲: D12 parents plus D1 houses 3/4/5/9 (siblings, mother/home, children, father).
+    "family": (("D12",), (3, 4, 5, 9)),
+    # Dated appearance/marks: D1 lagna / 1st house only. Not a primary formula.
+    "appearance": ((), (1,)),
 }
+AUXILIARY_DOMAINS: Final[frozenset[str]] = frozenset({"appearance"})
+AUXILIARY_SCORE_FACTOR: Final = 0.4
+
+
 class RectificationEventCalculationError(RuntimeError):
     """Raised when stored rectification evidence cannot be calculated safely."""
 
@@ -235,6 +243,9 @@ def _score_event(
                 points += 0.35
 
     event_kind = event.get("event_kind", event["domain"])
+    if event["domain"] in AUXILIARY_DOMAINS:
+        points *= AUXILIARY_SCORE_FACTOR
+        rules.append("appearance_auxiliary_not_primary")
     if not rules:
         rules.append("no_domain_activation")
     rules.append(f"event_kind:{event_kind}")
@@ -358,12 +369,12 @@ def build_candidate_static_context(
     charts = varga.calc_all_vargas(
         planet_longitudes,
         ascendant_longitude,
-        divisions=[2, 4, 9, 10, 24, 30],
+        divisions=[2, 4, 9, 10, 12, 24, 30],
     )
     d11_chart = _d11_chart(planet_longitudes, ascendant_longitude)
     varga_charts = {
         prefix: d11_chart if prefix == "D11" else _varga_chart(charts, prefix)
-        for prefix in ("D2", "D4", "D9", "D10", "D11", "D24", "D30")
+        for prefix in ("D2", "D4", "D9", "D10", "D11", "D12", "D24", "D30")
     }
     available_layers = ["D1"]
     blocked_layers = ["KP_cusps"]

scripts/active_rectification_events.py

@@ -35,6 +35,8 @@ EventDomain = Literal[
     "career",
     "finance",
     "health_pressure",
+    "family",
+    "appearance",
 ]
 Confidence = Literal["low", "medium", "high"]
 

scripts/rectification/contracts.py

@@ -27,9 +27,12 @@ EVENT_KINDS: dict[str, frozenset[str]] = {
     "health": frozenset({"self_health_event", "pressure_period"}),
     "health_pressure": frozenset({"self_health_event", "pressure_period"}),  # v1 domain compatibility
     "family": frozenset({"family_event"}),
+    "appearance": frozenset({"appearance_note"}),
+    "marks": frozenset({"birthmark_or_scar"}),
     "other": frozenset({"other"}),
 }
-BACKGROUND_EVENT_KINDS = frozenset({"family_event", "other"})
+BACKGROUND_EVENT_KINDS = frozenset({"other"})
+AUXILIARY_EVENT_KINDS = frozenset({"appearance_note", "birthmark_or_scar"})
 SCOREABLE_EVENT_KINDS: dict[str, frozenset[str]] = {
     domain: frozenset(kind for kind in kinds if kind not in BACKGROUND_EVENT_KINDS)
     for domain, kinds in EVENT_KINDS.items()
@@ -85,6 +88,21 @@ def is_scoreable_event(event: LifeEvent) -> bool:
     return event["event_kind"] not in BACKGROUND_EVENT_KINDS
 
 
+def is_primary_scoreable_event(event: LifeEvent) -> bool:
+    """Dated events that may move the candidate ranking as a primary formula."""
+    return is_scoreable_event(event) and event["event_kind"] not in AUXILIARY_EVENT_KINDS
+
+
+def subject_for_event_domain(domain: str, subject: str | None = None) -> Literal["self", "family", "other"]:
+    if domain == "family":
+        return "family"
+    if domain == "other":
+        return "other"
+    if subject in {"self", "family", "other"}:
+        return cast(Literal["self", "family", "other"], subject)
+    return "self"
+
+
 def _bounded_number(body: dict[str, Any], name: str, minimum: float, maximum: float) -> float:
     value = body.get(name)
     if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(float(value)):
@@ -167,12 +185,11 @@ def normalize_rectification_request(body: Any, *, today: date | None = None) ->
         summary = raw_event.get("summary", "")
         if not isinstance(summary, str) or len(summary) > 1_000:
             raise ValueError(f"events[{index}].summary must be a string up to 1000 characters")
-        subject = raw_event.get("subject")
-        if subject is None:
-            subject = "family" if domain == "family" else "other" if domain == "other" else "self"
-        if subject not in {"self", "family", "other"}:
+        raw_subject = raw_event.get("subject")
+        if raw_subject is not None and raw_subject not in {"self", "family", "other"}:
             raise ValueError(f"events[{index}].subject is invalid")
-        if event_kind not in BACKGROUND_EVENT_KINDS and subject != "self":
+        subject = subject_for_event_domain(cast(str, domain), None if raw_subject is None else str(raw_subject))
+        if event_kind not in BACKGROUND_EVENT_KINDS and domain != "family" and subject != "self":
             raise ValueError(f"events[{index}].subject must be self for scoreable events")
         cleaned_event: dict[str, Any] = {
             "id": event_id,

scripts/rectification/decision_policy.py

@@ -9,6 +9,7 @@ from scripts.active_rectification_events import CandidateScoreRow
 from scripts.rectification.contracts import (
     EVENT_CONTRACT_VERSION,
     RectificationRequest,
+    is_primary_scoreable_event,
     is_scoreable_event,
 )
 from scripts.rectification.house_table import compact_house_table_from_contexts
@@ -146,7 +147,7 @@ def build_decision_receipt(
     built: dict[str, Any],
     diagnostics: dict[str, Any],
 ) -> dict[str, Any]:
-    scoreable_events = [event for event in request["events"] if is_scoreable_event(event)]
+    scoreable_events = [event for event in request["events"] if is_primary_scoreable_event(event)]
     domains = sorted({event["domain"] for event in scoreable_events})
     candidate_presence = _gate(bool(candidate_decisions), candidate_count=len(candidate_decisions))
     event_quality = _gate(

scripts/rectification/refinement_packet.py

@@ -113,7 +113,7 @@ def _split_track_points(rule_ids: Sequence[str], points: float) -> tuple[float,
     return points * vim / total, points * narayana / total
 
 
-_LAYER_LABEL = {"d1": "本命上升", "d9": "D9", "d10": "D10", "d4": "D4"}
+_LAYER_LABEL = {"d1": "本命上升", "d9": "D9", "d10": "D10", "d4": "D4", "d12": "D12"}
 
 
 def window_scan(built: dict[str, Any]) -> dict[str, Any]:
@@ -128,6 +128,7 @@ def window_scan(built: dict[str, Any]) -> dict[str, Any]:
             "d9": vargas.get("D9") if isinstance(vargas.get("D9"), int) else None,
             "d10": vargas.get("D10") if isinstance(vargas.get("D10"), int) else None,
             "d4": vargas.get("D4") if isinstance(vargas.get("D4"), int) else None,
+            "d12": vargas.get("D12") if isinstance(vargas.get("D12"), int) else None,
         }
         for layer, bucket in counts.items():
             value = current[layer]
@@ -153,10 +154,12 @@ def window_scan(built: dict[str, Any]) -> dict[str, Any]:
         "d9_lagna_count": len(counts["d9"]),
         "d10_lagna_count": len(counts["d10"]),
         "d4_lagna_count": len(counts["d4"]),
+        "d12_lagna_count": len(counts["d12"]),
         "d1_candidates_differ": len(counts["d1"]) > 1,
         "d9_candidates_differ": len(counts["d9"]) > 1,
         "d10_candidates_differ": len(counts["d10"]) > 1,
         "d4_candidates_differ": len(counts["d4"]) > 1,
+        "d12_candidates_differ": len(counts["d12"]) > 1,
         "transitions": transitions,
     }
 

scripts/rectification/scoring_service.py

@@ -12,7 +12,7 @@ from scripts.active_rectification_event_engine import compute_candidate_static_c
 from scripts.active_rectification_events import CandidateScoreRow
 from scripts.rectification.contracts import LifeEvent, RectificationRequest, is_scoreable_event
 
-ALGORITHM_VERSION = "rectification-v5-matrix-scoring-2"
+ALGORITHM_VERSION = "rectification-v5-matrix-scoring-3"
 INPUT_CONTRACT_VERSION = "rectification-calculation-spec-v4"
 PRECISION_WEIGHTS = {
     "day": 1.0,
@@ -50,6 +50,9 @@ _ENGINE_KIND_BY_NATIVE_KIND: dict[str, tuple[str, str]] = {
     "finance_change": ("finance", "finance_change"),
     "self_health_event": ("health_pressure", "self_health_event"),
     "pressure_period": ("health_pressure", "self_health_event"),
+    "family_event": ("family", "family_event"),
+    "appearance_note": ("appearance", "appearance_note"),
+    "birthmark_or_scar": ("appearance", "birthmark_or_scar"),
 }
 
 
@@ -161,6 +164,9 @@ _KIND_SEMANTICS: dict[str, tuple[int, float]] = {
     "asset_change": (0, 1.0),
     "self_health_event": (-1, 1.0),
     "pressure_period": (-1, 1.2),
+    "family_event": (0, 1.0),
+    "appearance_note": (0, 0.8),
+    "birthmark_or_scar": (-1, 0.8),
 }
 
 
@@ -168,6 +174,22 @@ def precision_weight(precision: str) -> float:
     return PRECISION_WEIGHTS[precision]
 
 
+def public_technique_layers(domain: str, rule_ids: Sequence[str]) -> list[str]:
+    """Public methods actually computed for this event. Career always lists D1-10 and D10."""
+    layers = {
+        rule.split(":", 1)[0]
+        for rule in rule_ids
+        if not rule.startswith(("event_kind:", "event_kind_profile:"))
+    }
+    if domain == "career":
+        layers.update({"d1-rashi", "d10-dashamsa"})
+    elif domain == "family":
+        layers.update({"d1-rashi", "d12-dwadashamsha"})
+    elif domain in {"appearance", "marks"}:
+        layers.add("d1-rashi")
+    return sorted(layers)
+
+
 def _event_kind_factor(event_kind: str, rule_ids: Sequence[str]) -> float:
     direction, intensity = _KIND_SEMANTICS.get(event_kind, (0, 1.0))
     support = sum(any(rule.endswith(marker) for marker in _SUPPORT_RULES) for rule in rule_ids)
@@ -237,12 +259,10 @@ def build_event_contribution_matrix(
             matrix[event["id"]][candidate_time] = {
                 "points": round(sum(points) / len(points), 4),
                 "rule_ids": sorted({rule for item in evidences for rule in item["rule_ids"]}),
-                "technique_layers": sorted({
-                    rule.split(":", 1)[0]
-                    for item in evidences
-                    for rule in item["rule_ids"]
-                    if not rule.startswith(("event_kind:", "event_kind_profile:"))
-                }),
+                "technique_layers": public_technique_layers(
+                    event["domain"],
+                    [rule for item in evidences for rule in item["rule_ids"]],
+                ),
             }
         winner = max(set(winners), key=winners.count)
         mean = sum(matrix[event["id"]][time]["points"] for time in candidate_grid) / len(candidate_grid)

scripts/rectification_technique_contract.py

@@ -80,7 +80,7 @@ def build_rectification_technique_contract(
     return {
         "schema_version": 2,
         "calculation_status": "not_started" if event_count == 0 else "evaluated",
-        "used_divisional_charts": ["D2", "D4", "D9", "D10", "D11", "D24", "D30"],
+        "used_divisional_charts": ["D2", "D4", "D9", "D10", "D11", "D12", "D24", "D30"],
         "used_arudha": ["A7", "UL", "A10"],
         "dasha_tracks": ["vimshottari_md_ad_pd", "narayana_md_ad"],
         "missing_layers": reported_missing_layers,