Multiple fixes: lead matching, metrics aggregation, Log field, SPRINT with capping=0

- Fix GoogleCampaignID matching with comma delimiters (exact word boundary)
- Remove attr_utm_medium filter (campaign ID filter is specific enough)
- Fix get_campaign_metrics: accumulate all daily rows for monthly totals
- Fix SPRINT urgency: only trigger when capping > 0
- Add Log field to GACampaignMes with Airtable vs Google Ads discrepancy info
- batch_update_gacampaignmes_advice now also updates Log field

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

airtable_client.py

@@ -344,10 +344,9 @@ class AirtableClient:
             f"AND("
             f"OR("
             f"{{GACampaignID}}='{campaign_id}',"
-            f"FIND('{campaign_id}',{{GoogleCampaignID}}),"
+            f"FIND(',{campaign_id},',',' & {{GoogleCampaignID}} & ','),"
             f"FIND('gad_campaignid={campaign_id}',{{attr_referer}})"
             f"),"
-            f"{{attr_utm_medium}}='paid_search',"
             f"{{creado}}>='{mes_inicio}'"
             f")"
         )
@@ -372,16 +371,16 @@ class AirtableClient:
         for i in range(0, len(batch), 10):
             self.gacampaignmes.batch_update(batch[i:i+10])
 
-    def batch_update_gacampaignmes_advice(self, updates: list[tuple[str, str, str]]) -> None:
+    def batch_update_gacampaignmes_advice(self, updates: list[tuple]) -> None:
         """
-        Actualiza en lote los campos 'Consejo' y 'Criticidad' de GACampaignMes.
+        Actualiza en lote los campos 'Consejo', 'Criticidad' y 'Log' de GACampaignMes.
-        updates: lista de (gcm_record_id, consejo_text, criticidad)
+        updates: lista de (gcm_record_id, consejo_text, criticidad, log_text)
           criticidad: 'Crítico' | 'Peligro' | 'Mantener'
         """
         for i in range(0, len(updates), 10):
             batch = [
-                {"id": rid, "fields": {"Consejo": consejo, "Criticidad": criticidad}}
+                {"id": rid, "fields": {"Consejo": consejo, "Criticidad": criticidad, "Log": log}}
-                for rid, consejo, criticidad in updates[i:i+10]
+                for rid, consejo, criticidad, log in updates[i:i+10]
             ]
             if batch:
                 self.gacampaignmes.batch_update(batch, typecast=True)

analyzer.py

@@ -26,7 +26,7 @@ def analyze(campaign_config: dict, leads_entregados: int, ads_metrics: dict) ->
     # Urgencia
     if ratio_leads >= 1.0:
         urgencia = "PAUSAR"
-    elif ratio_leads < ratio_mes - 0.15 and dias_restantes <= 5:
+    elif capping > 0 and ratio_leads < ratio_mes - 0.15 and dias_restantes <= 5:
         urgencia = "SPRINT"
     elif ritmo < -0.15:
         urgencia = "ACELERAR"

google_ads_client.py

@@ -70,7 +70,7 @@ class GoogleAdsClient:
         return result
 
     def get_campaign_metrics(self, campaign_id: str) -> dict:
-        """Métricas del mes en curso para una campaña concreta."""
+        """Métricas del mes en curso para una campaña concreta (acumulado mensual)."""
         ga_service = self.client.get_service("GoogleAdsService")
         query = f"""
             SELECT
@@ -82,27 +82,42 @@ class GoogleAdsClient:
                 metrics.cost_micros,
                 metrics.conversions,
                 metrics.clicks,
-                metrics.impressions,
+                metrics.impressions
-                metrics.ctr
             FROM campaign
             WHERE campaign.id = {campaign_id}
               AND segments.date DURING THIS_MONTH
         """
         try:
             response = ga_service.search(customer_id=self.customer_id, query=query)
+            cost = conversions = clicks = impressions = 0
+            meta = {}
             for row in response:
                 m = row.metrics
-                return {
+                cost        += m.cost_micros / 1_000_000
-                    "campaign_id": campaign_id,
+                conversions += m.conversions
+                clicks      += m.clicks
+                impressions += m.impressions
+                if not meta:
+                    meta = {
                         "name":                 row.campaign.name,
                         "status":               row.campaign.status.name,
                         "budget_daily":         row.campaign_budget.amount_micros / 1_000_000,
                         "budget_resource_name": row.campaign_budget.resource_name,
-                    "cost": m.cost_micros / 1_000_000,
+                    }
-                    "conversions": m.conversions,
+            if not meta:
-                    "clicks": m.clicks,
+                return {}
-                    "impressions": m.impressions,
+            ctr = round(clicks / impressions * 100, 2) if impressions > 0 else 0.0
-                    "ctr": round(m.ctr * 100, 2),
+            return {
+                "campaign_id":          campaign_id,
+                "name":                 meta["name"],
+                "status":               meta["status"],
+                "budget_daily":         meta["budget_daily"],
+                "budget_resource_name": meta["budget_resource_name"],
+                "cost":                 round(cost, 2),
+                "conversions":          conversions,
+                "clicks":               clicks,
+                "impressions":          impressions,
+                "ctr":                  ctr,
             }
         except GoogleAdsException as e:
             print(f"  ❌ Error Google Ads para campaña {campaign_id}: {e}")

run.py

@@ -225,10 +225,21 @@ def run():
 
         apply_decision(campaign, decision, metrics, gads)
 
+        conv_google = int(analysis["conversiones_google"])
+        discrepancia_fresh = abs(conv_google - leads)
+        if discrepancia_fresh > 10:
+            log_text = (
+                f"⚠️ DISCREPANCIA: {leads} leads Airtable vs {conv_google} conv Google Ads "
+                f"(Δ {discrepancia_fresh})"
+            )
+        else:
+            log_text = f"OK: {leads} leads Airtable | {conv_google} conv Google Ads"
+
         advice_updates.append((
             campaign["airtable_id"],
             decision.get("consejo", ""),
             CRITICIDAD_MAP[p],
+            log_text,
         ))
 
         resumen.append({