Fix leadform double-attribution and Slack message truncation

- airtable_client: restrict leadform cursoid path (path 5) to PMX-only
  campaigns, preventing triple-attribution to search+pmx+leadform companion
- slack_reporter: derive margen from ingreso-coste instead of stale MetricasDiarias
  field; split monthly rankings from fields to separate section blocks; add
  _trunc() helper to clamp all text blocks under Slack's 3000-char limit
- agent: increase portfolio_daily_analysis max_tokens 500→800
- backfill: extend to June 11

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

agent.py

@@ -141,7 +141,7 @@ def portfolio_daily_analysis(collected: list) -> str:
     try:
         response = client.messages.create(
             model="claude-sonnet-4-6",
-            max_tokens=500,
+            max_tokens=800,
             system=PORTFOLIO_SYSTEM,
             messages=[{
                 "role": "user",

airtable_client.py

@@ -359,7 +359,10 @@ class AirtableClient:
         m = re.search(r'fco_(?:search|pmx)_(\d+)', campaign_name, re.IGNORECASE)
         if m:
             course_num = m.group(1)
-            if "pmx" in campaign_name.lower() and "_leadform" not in campaign_name.lower():
+            is_pmx_non_leadform = (
+                "pmx" in campaign_name.lower() and "_leadform" not in campaign_name.lower()
+            )
+            if is_pmx_non_leadform:
                 utm_source = "pmx"
             elif "search" in campaign_name.lower():
                 utm_source = "google"
@@ -370,6 +373,8 @@ class AirtableClient:
                     f"AND({{attr_cursoid}}='{course_num}',"
                     f"{{attr_utm_source}}='{utm_source}')"
                 )
+            # Path 5: leadform leads en Airtable, solo para PMX (no search, no _leadform)
+            if is_pmx_non_leadform:
                 leadform_cursoid_clause = (
                     f"AND({{attr_cursoid}}='{course_num}',"
                     f"{{UserAgent del visitante}}='Google-Ads-Notifications')"

backfill_leadform_jun8_10.py

@@ -8,7 +8,7 @@ import re
 from datetime import datetime
 from airtable_client import AirtableClient
 
-DAYS = ["2026-06-08", "2026-06-09", "2026-06-10"]
+DAYS = ["2026-06-08", "2026-06-09", "2026-06-10", "2026-06-11"]
 
 
 def _course_num(name: str) -> str | None:

slack_reporter.py

@@ -46,14 +46,20 @@ def _last_n_days_combined(current_md: dict, prev_md: dict, now: datetime, n: int
 
     entries.sort(key=lambda x: x[0])
     last_n = entries[-n:]
+    total_coste  = round(sum(v.get("coste",   0) for _, v in last_n), 2)
+    total_ingreso = round(sum(v.get("ingreso", 0) for _, v in last_n), 2)
     return {
-        "coste":   round(sum(v.get("coste",   0) for _, v in last_n), 2),
-        "ingreso": round(sum(v.get("ingreso", 0) for _, v in last_n), 2),
-        "margen":  round(sum(v.get("margen",  0) for _, v in last_n), 2),
+        "coste":   total_coste,
+        "ingreso": total_ingreso,
+        "margen":  round(total_ingreso - total_coste, 2),
         "n_days":  len(last_n),
     }
 
 
+def _trunc(text: str, limit: int = 2950) -> str:
+    return text if len(text) <= limit else text[:limit - 3] + "…"
+
+
 def _fmt_eur(v: float) -> str:
     sign = "+" if v > 0 else ""
     return f"{sign}{v:,.0f}€".replace(",", ".")
@@ -195,7 +201,7 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
             "type": "section",
             "text": {
                 "type": "mrkdwn",
-                "text": f"📅 *MÁRGENES POR DÍA — {MESES_ES[now.month].upper()}*\n```\n" + "\n".join(rows) + "\n```",
+                "text": _trunc(f"📅 *MÁRGENES POR DÍA — {MESES_ES[now.month].upper()}*\n```\n" + "\n".join(rows) + "\n```"),
             },
         }
 
@@ -289,14 +295,14 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
         "type": "section",
         "text": {
             "type": "mrkdwn",
-            "text": _ranking_last5_text(worst_last5, "📉 *PEOR — ÚLTIMOS 5 DÍAS*"),
+            "text": _trunc(_ranking_last5_text(worst_last5, "📉 *PEOR — ÚLTIMOS 5 DÍAS*")),
         },
     })
     blocks.append({
         "type": "section",
         "text": {
             "type": "mrkdwn",
-            "text": _ranking_last5_text(best_last5, "📈 *MEJOR — ÚLTIMOS 5 DÍAS*"),
+            "text": _trunc(_ranking_last5_text(best_last5, "📈 *MEJOR — ÚLTIMOS 5 DÍAS*")),
         },
     })
 
@@ -304,16 +310,17 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
         blocks.append({"type": "divider"})
         blocks.append({
             "type": "section",
-            "fields": [
-                {
+            "text": {
                 "type": "mrkdwn",
-                    "text": _ranking_month_text(worst_month, "📉 *PEOR — MES EN CURSO*"),
+                "text": _trunc(_ranking_month_text(worst_month, "📉 *PEOR — MES EN CURSO*")),
             },
-                {
+        })
+        blocks.append({
+            "type": "section",
+            "text": {
                 "type": "mrkdwn",
-                    "text": _ranking_month_text(best_month, "📈 *MEJOR — MES EN CURSO*"),
+                "text": _trunc(_ranking_month_text(best_month, "📈 *MEJOR — MES EN CURSO*")),
             },
-            ],
         })
 
     if portfolio_analysis_text:
@@ -322,7 +329,7 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
             "type": "section",
             "text": {
                 "type": "mrkdwn",
-                "text": f"🤖 *DIAGNÓSTICO ESTRATÉGICO*\n{portfolio_analysis_text}",
+                "text": _trunc(f"🤖 *DIAGNÓSTICO ESTRATÉGICO*\n{portfolio_analysis_text}"),
             },
         })