Add leadform campaign detection and log notes for attribution discrepancies

- Detect courses with _leadform companion campaigns before first pass
- Add ℹ️ LEADFORM note in Log for campaigns that use Google's native lead form (leads bypass the website and don't reach Airtable)
- Add ⚠️ LEADFORM COMPANION note for sibling campaigns of the same course (explains conversion count discrepancy)
- Also moves _course_num and courses_with_both detection to pre-pass (before first loop) so PMX analysis uses correct leads_grupo

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

run.py

@@ -125,6 +125,32 @@ def run():
     print(f"→ {len(campaigns)} campañas con actividad este mes")
     print("→ Analizando...\n")
 
+    # Detección anticipada de cursos con Search Y PMX simultáneos
+    def _course_num(name: str) -> str | None:
+        m = re.search(r'fco_(?:search|pmx)_(\d+)', name, re.IGNORECASE)
+        return m.group(1) if m else None
+
+    course_types_pre: dict[str, set] = {}
+    for c in campaigns:
+        num = _course_num(c["curso"])
+        if num:
+            course_types_pre.setdefault(num, set())
+            if "pmx" in c["curso"].lower():
+                course_types_pre[num].add("pmx")
+            elif "search" in c["curso"].lower():
+                course_types_pre[num].add("search")
+    courses_with_both = {
+        num for num, types in course_types_pre.items()
+        if "pmx" in types and "search" in types
+    }
+
+    # Detección anticipada de cursos con campaña _leadform activa
+    courses_with_leadform = {
+        _course_num(c["curso"])
+        for c in campaigns
+        if "_leadform" in c["curso"].lower() and _course_num(c["curso"])
+    }
+
     # === PRIMERA PASADA: recopilar datos de todas las campañas ===
     collected = []
     skipped = []
@@ -150,12 +176,21 @@ def run():
                 google_status,
             ))
 
-        analysis = analyze(campaign, leads, metrics)
+        # Para PMX con companion Search: usar conversiones Google como leads de análisis
+        course_num = _course_num(campaign["curso"])
+        is_pmx_with_companion = (
+            "pmx" in campaign["curso"].lower()
+            and course_num in courses_with_both
+        )
+        leads_grupo = int(metrics.get("conversions", 0)) if is_pmx_with_companion else leads
+
+        analysis = analyze(campaign, leads_grupo, metrics)
         decision = decide(analysis)
 
         collected.append({
             "campaign": campaign,
             "leads": leads,
+            "leads_grupo": leads_grupo,
             "metrics": metrics,
             "analysis": analysis,
             "decision": decision,
@@ -174,23 +209,6 @@ def run():
     # Ordenar: 0=críticas → 1=no críticas → 2=mantener
     collected.sort(key=_priority)
 
-    # Detectar cursos con campaña Search Y PMX simultáneas (posible reatribución PMX)
-    def _course_num(name: str) -> str | None:
-        m = re.search(r'fco_(?:search|pmx)_(\d+)', name, re.IGNORECASE)
-        return m.group(1) if m else None
-
-    course_types: dict[str, set] = {}
-    for item in collected:
-        name = item["campaign"]["curso"]
-        num = _course_num(name)
-        if num:
-            course_types.setdefault(num, set())
-            if "pmx" in name.lower():
-                course_types[num].add("pmx")
-            elif "search" in name.lower():
-                course_types[num].add("search")
-    courses_with_both = {num for num, types in course_types.items() if "pmx" in types and "search" in types}
-
     # === SEGUNDA PASADA: imprimir en orden + aplicar decisiones ===
     resumen = []
     advice_updates = []   # (gcm_record_id, consejo, criticidad) para batch update final
@@ -258,6 +276,13 @@ def run():
         if "search" in campaign["curso"].lower() and course_num in courses_with_both:
             log_text += " | ⚠️ PMX ATTRIBUTION: campaña Search con companion PMX activo — parte de las conversiones de Google pueden estar reatribuidas a la campaña PMX"
 
+        # Nota leadform: los leads se capturan en Google sin pasar por la web
+        is_leadform = "_leadform" in campaign["curso"].lower()
+        if is_leadform:
+            log_text += " | ℹ️ LEADFORM: leads capturados directamente en Google (sin visitar la web) — no llegan a Airtable"
+        elif course_num in courses_with_leadform:
+            log_text += " | ⚠️ LEADFORM COMPANION: existe una campaña _leadform activa para este curso — parte de las conversiones de Google pueden provenir de leads capturados directamente en Google"
+
         advice_updates.append((
             campaign["airtable_id"],
             decision.get("consejo", ""),
@@ -286,26 +311,15 @@ def run():
         print("  ✓ Consejos y criticidad guardados.")
 
     # Snapshot diario: ConvLeadsLakeMesFinal + ConvLeadsLakeMesGrupo
-    # Para PMX con Search companion del mismo curso, Grupo = leads del Search
+    # PMX con companion Search → Grupo = conversiones Google (ya calculado en leads_grupo)
-    course_search_leads = {
+    final_leads_data = [
-        _course_num(item["campaign"]["curso"]): item["leads"]
+        {
-        for item in collected
-        if "search" in item["campaign"]["curso"].lower()
-        and _course_num(item["campaign"]["curso"])
-    }
-    final_leads_data = []
-    for item in collected:
-        name = item["campaign"]["curso"]
-        num = _course_num(name)
-        if num and "pmx" in name.lower() and num in courses_with_both:
-            grupo = course_search_leads.get(num, item["leads"])
-        else:
-            grupo = item["leads"]
-        final_leads_data.append({
             "airtable_id": item["campaign"]["airtable_id"],
             "conv_leads_lake_mes": item["leads"],
-            "conv_leads_lake_mes_grupo": grupo,
+            "conv_leads_lake_mes_grupo": item["leads_grupo"],
-        })
+        }
+        for item in collected
+    ]
     if final_leads_data:
         print(f"→ Actualizando ConvLeadsLakeMesFinal ({len(final_leads_data)} registros)...")
         at.batch_update_gacampaignmes_final_leads(final_leads_data)