feat(product): present consult and rectification in local skill form

Default ayanamsa to Raman with true_pushya support, attach governed Raman packets, restore Path C questionnaires and eight-method verification copy, and keep unique-minute confirmation blocked.

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

scripts/rectification/decision_policy.py

@@ -56,8 +56,8 @@ _AUDIT_LABELS = {
     "d5-panchamsha": ("D5 成就分盘", "本轮已对照学业或被委以责任的变化。"),
     "d3-drekkana": ("D3 兄弟分盘", "本轮已对照兄弟姐妹主题。"),
     "d7-saptamsha": ("D7 子女分盘", "本轮已对照子女或伴侣细节。"),
-    "d9-navamsa": ("D9 婚姻分盘", "本轮已对照关系主题，未给类型标签。"),
-    "d10-dashamsa": ("D10 事业分盘", "本轮已对照事业主题，未给类型标签。"),
+    "d9-navamsa": ("D9 婚姻分盘", "本轮已对照关系主题，可用 D9 上升类型表作校时方法，不是命运承诺。"),
+    "d10-dashamsa": ("D10 事业分盘", "本轮已对照事业主题，可用 D10 上升类型表作校时方法，不是命运承诺。"),
     "d11-labhamsha": ("D11 收益分盘", "本轮已对照收益主题。"),
     "d12-dwadashamsha": ("D12 父母分盘", "本轮已对照家人主题。"),
     "d24-chaturvimshamsha": ("D24 教育分盘", "本轮已对照学业主题。"),
@@ -596,6 +596,7 @@ def build_decision_receipt(
     receipt.update({
         "window_scan": packet["window_scan"],
         "event_dasha_ledger": packet["event_dasha_ledger"],
+        "event_fit_rate": packet["event_fit_rate"],
         "dasha_agreement": packet["dasha_agreement"],
         "lagna_contrast": packet["lagna_contrast"],
         "nakshatra_boundary": packet["nakshatra_boundary"],

scripts/rectification/horary_observation.py

@@ -61,7 +61,7 @@ def build_horary_observation(request: dict[str, Any]) -> dict[str, Any]:
             "lat": float(request["lat"]),
             "lon": float(request["lon"]),
             "tz": float(request["tz"]),
-            "ayanamsa": "lahiri",
+            "ayanamsa": "raman",
             "node_mode": "mean",
         })
         asc = chart.get("ascendant") or {}

scripts/rectification/kp_cusp_observation.py

@@ -9,7 +9,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from ayanamsa_utils import apply_ayanamsa, current_ayanamsa_name
+from ayanamsa_utils import DEFAULT_AYANAMSA_NAME, apply_ayanamsa, current_ayanamsa_name
 from kp_system import KP_LORDS, get_kp_lords
 from scripts.domain_calculation_service import swiss_ephemeris_lock
 
@@ -84,4 +84,4 @@ def observe_kp_cusps(jd: float | None, lat: float | None, lon: float | None) ->
         except (TypeError, ValueError, OverflowError, OSError, RuntimeError, ArithmeticError):
             return dict(_BLOCKED)
         finally:
-            apply_ayanamsa(previous or "lahiri", swe)
+            apply_ayanamsa(previous or DEFAULT_AYANAMSA_NAME, swe)

scripts/rectification/refinement_packet.py

@@ -1,7 +1,8 @@
 """Server-owned P0/P1 refinement packet for birth-time rectification.
 
-Produces candidate-narrowing structure only. Never grants a unique minute,
-never emits D9/D10 type labels, and never copies raw scores into public copy.
+Produces candidate-narrowing structure only. Never grants a unique minute
+and never copies raw scores into public copy. D9/D10 sign names are method
+contrast for the skill verification report, not unique-minute proof.
 """
 
 from __future__ import annotations
@@ -76,6 +77,18 @@ def _features(built: dict[str, Any]) -> list[dict[str, Any]]:
     return rows
 
 
+def _sign_names(indices: set[int]) -> list[str]:
+    names: list[str] = []
+    for idx in sorted(indices):
+        if isinstance(idx, int) and 0 <= idx <= 11:
+            names.append(SIGNS_CN[SIGNS[idx]])
+    return names
+
+
+def _has_gochara(rule_ids: Sequence[str]) -> bool:
+    return any("controlled_transit" in str(item) or str(item).startswith("gochara") for item in rule_ids)
+
+
 def match_level(rule_ids: Sequence[str]) -> str:
     ids = [str(item) for item in rule_ids]
     if not ids or ids == ["no_domain_activation"]:
@@ -92,6 +105,8 @@ def match_level(rule_ids: Sequence[str]) -> str:
         return "medium"
     if any(item.startswith("vim_") or item.startswith("narayana_") for item in ids):
         return "weak"
+    if _has_gochara(ids):
+        return "medium"
     return "none"
 
 
@@ -101,6 +116,8 @@ def _tracks(rule_ids: Sequence[str]) -> list[str]:
         tracks.append("vimshottari")
     if any(str(item).startswith("narayana_") for item in rule_ids):
         tracks.append("narayana")
+    if _has_gochara(rule_ids):
+        tracks.append("gochara")
     return tracks
 
 
@@ -196,6 +213,8 @@ def window_scan(built: dict[str, Any]) -> dict[str, Any]:
     for layer in _LAYER_LABEL:
         payload[f"{layer}_lagna_count" if layer.startswith("d") else f"{layer}_count"] = len(counts[layer])
         payload[f"{layer}_candidates_differ"] = len(counts[layer]) > 1
+    payload["d9_sign_names"] = _sign_names(counts["d9"])
+    payload["d10_sign_names"] = _sign_names(counts["d10"])
     return payload
 
 
@@ -219,18 +238,58 @@ def event_dasha_ledger(
         summary = str(event.get("summary") or "").strip() or "这条经历"
         tracks = _tracks(rule_ids)
         track_text = "、".join(
-            "主限" if track == "vimshottari" else "分盘大运" for track in tracks
+            "主限" if track == "vimshottari" else "分盘大运" if track == "narayana" else "受控行运"
+            for track in tracks
         ) or "现有大运层"
+        gochara_hit = _has_gochara(rule_ids)
         rows.append({
             "summary": summary[:80],
             "match": level,
             "match_label": MATCH_LABELS[level],
             "tracks": tracks,
-            "user_meaning": f"{summary[:40]}：{MATCH_LABELS[level]}（{track_text}）",
+            "gochara": "activated" if gochara_hit else "not_seen",
+            "user_meaning": (
+                f"{summary[:40]}：{MATCH_LABELS[level]}（{track_text}"
+                f"{'；Gochara 激活相关宫' if gochara_hit else '；Gochara 未见对应'}）"
+            ),
         })
     return rows
 
 
+def event_fit_rate(rows: Sequence[dict[str, Any]]) -> dict[str, Any]:
+    total = len(rows)
+    matched = sum(1 for row in rows if row.get("match") in {"strong", "medium"})
+    if total == 0:
+        return {
+            "matched": 0,
+            "total": 0,
+            "percent": None,
+            "band": "insufficient",
+            "label": "事件不足，无法计算吻合率",
+            "unique_minute_claim": False,
+            "user_meaning": "事件–Dasha–Gochara 表还没有可评分行。这不是唯一分钟确认。",
+        }
+    percent = round(100 * matched / total)
+    band = "high" if percent >= 80 else "medium" if percent >= 60 else "low"
+    label = (
+        "高度吻合（事件吻合率 ≥80%）" if band == "high"
+        else "中度吻合（事件吻合率 60–80%）" if band == "medium"
+        else "低度吻合（事件吻合率 <60%）"
+    )
+    return {
+        "matched": matched,
+        "total": total,
+        "percent": percent,
+        "band": band,
+        "label": label,
+        "unique_minute_claim": False,
+        "user_meaning": (
+            f"当前窗 {matched}/{total} 件已确认事件与 Dasha/Gochara 吻合，{label}。"
+            "这是相对拟合，不是已确认唯一出生分钟。"
+        ),
+    }
+
+
 def dasha_agreement(built: dict[str, Any], candidate_times: Sequence[str]) -> dict[str, Any]:
     times = [str(item)[:5] for item in candidate_times if isinstance(item, str) and len(str(item)) >= 5]
     if not times:
@@ -324,7 +383,7 @@ def lagna_contrast(built: dict[str, Any]) -> dict[str, Any] | None:
         "user_meaning": (
             f"窗口里出现两段本命上升：{left['start']}-{left['end']} 为{left['lagna']}，"
             f"{right['start']}-{right['end']} 为{right['lagna']}。"
-            "只比较宫主结构，不给性格或类型标签。"
+            "可并列 D9/D10 类型表作校时方法，不是命运承诺，也不能确认唯一分钟。"
         ),
         "unique_minute_claim": False,
     }
@@ -399,7 +458,7 @@ def precision_stage(scan: dict[str, Any], event_count: int) -> dict[str, Any]:
         meaning = "事业盘已较稳，居所盘仍会换升。可再补一件记得时间的搬家或住处变化。"
     elif scan.get("d5_candidates_differ") or scan.get("d24_candidates_differ"):
         current = "d5_refine"
-        meaning = "居所盘已较稳，成就盘或学业盘仍会换升。可再补一件记得时间的学业、考试或被委以责任的变化；不要贴类型标签。"
+        meaning = "居所盘已较稳，成就盘或学业盘仍会换升。可再补一件记得时间的学业、考试或被委以责任的变化。"
     else:
         current = "ready_to_adopt"
         meaning = "核心分盘已不再换升。可以采用代表性时间看盘，也可以再补主题经历。"
@@ -441,10 +500,12 @@ def build_refinement_packet(
     candidate_times: Sequence[str],
 ) -> dict[str, Any]:
     scan = window_scan(built)
+    ledger = event_dasha_ledger(request, built, representative_time)
     agreement = dasha_agreement(built, candidate_times)
     return {
         "window_scan": scan,
-        "event_dasha_ledger": event_dasha_ledger(request, built, representative_time),
+        "event_dasha_ledger": ledger,
+        "event_fit_rate": event_fit_rate(ledger),
         "dasha_agreement": agreement,
         "lagna_contrast": lagna_contrast(built),
         "nakshatra_boundary": nakshatra_boundary(built, representative_time),

scripts/rectification/scoring_service.py

@@ -328,7 +328,7 @@ def calculation_spec(request: RectificationRequest) -> dict[str, Any]:
         "latitude": json_number(request["lat"]),
         "longitude": json_number(request["lon"]),
         "timezoneOffsetHours": json_number(request["tz"]),
-        "ayanamsa": "lahiri", "nodeMode": "mean", "minuteStep": 1,
+        "ayanamsa": "raman", "nodeMode": "mean", "minuteStep": 1,
     }
     for source, target in (
         ("birth_time_source", "birthTimeSource"),