[30388f42] Infrastructure Hardening: Repaired CE/Connector DB schema, fixed frontend styling build, implemented robust echo shield in worker v2.1.1, and integrated Lead Engine into gateway.

.dev_session/SESSION_INFO

@@ -1 +1 @@
-{"task_id": "2ff88f42-8544-8000-8314-c9013414d1d0", "token": "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", "session_start_time": "2026-02-19T08:32:53.260193"}
+{"task_id": "30388f42-8544-8088-bc48-e59e9b973e91", "token": "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", "readme_path": null, "session_start_time": "2026-03-07T14:07:47.125445"}

ARCHIVE_legacy_scripts/check_benni.py

@@ -0,0 +1,40 @@
+import sqlite3
+import os
+import json
+
+DB_PATH = "companies_v3_fixed_2.db"
+
+def check_company_33():
+    if not os.path.exists(DB_PATH):
+        print(f"❌ Database not found at {DB_PATH}")
+        return
+
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"🔍 Checking Company ID 33 (Bennis Playland)...")
+        # Check standard fields
+        cursor.execute("SELECT id, name, city, street, zip_code FROM companies WHERE id = 33")
+        row = cursor.fetchone()
+        if row:
+            print(f"  Standard: City='{row[2]}', Street='{row[3]}', Zip='{row[4]}'")
+        else:
+            print("  ❌ Company 33 not found in DB.")
+            
+        # Check Enrichment
+        cursor.execute("SELECT content FROM enrichment_data WHERE company_id = 33 AND source_type = 'website_scrape'")
+        enrich_row = cursor.fetchone()
+        if enrich_row:
+            data = json.loads(enrich_row[0])
+            imp = data.get("impressum")
+            print(f"  Impressum Data: {json.dumps(imp, indent=2) if imp else 'None'}")
+        else:
+            print("  ❌ No website_scrape found for Company 33.")
+            
+        conn.close()
+    except Exception as e:
+        print(f"❌ Error: {e}")
+
+if __name__ == "__main__":
+    check_company_33()

ARCHIVE_legacy_scripts/check_erding_openers.py

@@ -0,0 +1,16 @@
+import sqlite3
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+conn = sqlite3.connect(DB_PATH)
+cursor = conn.cursor()
+
+cursor.execute("SELECT name, ai_opener, ai_opener_secondary, industry_ai FROM companies WHERE name LIKE '%Erding%'")
+row = cursor.fetchone()
+if row:
+    print(f"Company: {row[0]}")
+    print(f"Industry: {row[3]}")
+    print(f"Opener Primary: {row[1]}")
+    print(f"Opener Secondary: {row[2]}")
+else:
+    print("Company not found.")
+conn.close()

ARCHIVE_legacy_scripts/check_klinikum_erding.py

@@ -0,0 +1,16 @@
+import sqlite3
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+conn = sqlite3.connect(DB_PATH)
+cursor = conn.cursor()
+
+cursor.execute("SELECT name, ai_opener, ai_opener_secondary, industry_ai FROM companies WHERE name LIKE '%Klinikum Landkreis Erding%'")
+row = cursor.fetchone()
+if row:
+    print(f"Company: {row[0]}")
+    print(f"Industry: {row[3]}")
+    print(f"Opener Primary: {row[1]}")
+    print(f"Opener Secondary: {row[2]}")
+else:
+    print("Company not found.")
+conn.close()

ARCHIVE_legacy_scripts/check_mappings.py

@@ -0,0 +1,14 @@
+import sqlite3
+
+def check_mappings():
+    conn = sqlite3.connect('/app/companies_v3_fixed_2.db')
+    cursor = conn.cursor()
+    cursor.execute("SELECT * FROM job_role_mappings")
+    rows = cursor.fetchall()
+    print("--- Job Role Mappings ---")
+    for row in rows:
+        print(row)
+    conn.close()
+
+if __name__ == "__main__":
+    check_mappings()

ARCHIVE_legacy_scripts/check_matrix.py

@@ -0,0 +1,25 @@
+import os
+import sys
+
+# Add the company-explorer directory to the Python path
+sys.path.append(os.path.abspath(os.path.join(os.getcwd(), 'company-explorer')))
+
+from backend.database import SessionLocal, MarketingMatrix, Industry, Persona
+import json
+
+db = SessionLocal()
+try:
+    count = db.query(MarketingMatrix).count()
+    print(f"MarketingMatrix count: {count}")
+    
+    if count > 0:
+        first = db.query(MarketingMatrix).first()
+        print(f"First entry: ID={first.id}, Industry={first.industry_id}, Persona={first.persona_id}")
+    else:
+        print("MarketingMatrix is empty.")
+        # Check if we have industries and personas
+        ind_count = db.query(Industry).count()
+        pers_count = db.query(Persona).count()
+        print(f"Industries: {ind_count}, Personas: {pers_count}")
+finally:
+    db.close()

ARCHIVE_legacy_scripts/check_matrix_indoor.py

@@ -0,0 +1,23 @@
+import sqlite3
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+conn = sqlite3.connect(DB_PATH)
+cursor = conn.cursor()
+
+query = """
+SELECT i.name, p.name, m.subject, m.intro, m.social_proof 
+FROM marketing_matrix m
+JOIN industries i ON m.industry_id = i.id
+JOIN personas p ON m.persona_id = p.id
+WHERE i.name = 'Leisure - Indoor Active'
+"""
+
+cursor.execute(query)
+rows = cursor.fetchall()
+for row in rows:
+    print(f"Industry: {row[0]} | Persona: {row[1]}")
+    print(f"  Subject: {row[2]}")
+    print(f"  Intro: {row[3]}")
+    print(f"  Social Proof: {row[4]}")
+    print("-" * 50)
+conn.close()

ARCHIVE_legacy_scripts/check_matrix_results.py

@@ -0,0 +1,24 @@
+import sqlite3
+import json
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+conn = sqlite3.connect(DB_PATH)
+cursor = conn.cursor()
+
+query = """
+SELECT i.name, p.name, m.subject, m.intro, m.social_proof 
+FROM marketing_matrix m
+JOIN industries i ON m.industry_id = i.id
+JOIN personas p ON m.persona_id = p.id
+WHERE i.name = 'Healthcare - Hospital'
+"""
+
+cursor.execute(query)
+rows = cursor.fetchall()
+for row in rows:
+    print(f"Industry: {row[0]} | Persona: {row[1]}")
+    print(f"  Subject: {row[2]}")
+    print(f"  Intro: {row[3]}")
+    print(f"  Social Proof: {row[4]}")
+    print("-" * 50)
+conn.close()

ARCHIVE_legacy_scripts/check_silly_billy.py

@@ -0,0 +1,53 @@
+import sqlite3
+import os
+
+DB_PATH = "companies_v3_fixed_2.db"
+
+def check_company():
+    if not os.path.exists(DB_PATH):
+        print(f"❌ Database not found at {DB_PATH}")
+        return
+
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"🔍 Searching for 'Silly Billy' in {DB_PATH}...")
+        cursor.execute("SELECT id, name, crm_id, ai_opener, ai_opener_secondary, city, crm_vat, status FROM companies WHERE name LIKE '%Silly Billy%'")
+        rows = cursor.fetchall()
+        
+        if not rows:
+            print("❌ No company found matching 'Silly Billy'")
+        else:
+            for row in rows:
+                company_id = row[0]
+                print("\n✅ Company Found:")
+                print(f"  ID: {company_id}")
+                print(f"  Name: {row[1]}")
+                print(f"  CRM ID: {row[2]}")
+                print(f"  Status: {row[7]}")
+                print(f"  City: {row[5]}")
+                print(f"  VAT: {row[6]}")
+                print(f"  Opener (Primary): {row[3][:50]}..." if row[3] else "  Opener (Primary): None")
+                
+                # Check Enrichment Data
+                print(f"\n  🔍 Checking Enrichment Data for ID {company_id}...")
+                cursor.execute("SELECT content FROM enrichment_data WHERE company_id = ? AND source_type = 'website_scrape'", (company_id,))
+                enrich_row = cursor.fetchone()
+                if enrich_row:
+                    import json
+                    try:
+                        data = json.loads(enrich_row[0])
+                        imp = data.get("impressum")
+                        print(f"  Impressum Data in Scrape: {json.dumps(imp, indent=2) if imp else 'None'}")
+                    except Exception as e:
+                        print(f"  ❌ Error parsing JSON: {e}")
+                else:
+                    print("  ❌ No website_scrape enrichment data found.")
+            
+        conn.close()
+    except Exception as e:
+        print(f"❌ Error reading DB: {e}")
+
+if __name__ == "__main__":
+    check_company()

ARCHIVE_legacy_scripts/clear_zombies.py

@@ -0,0 +1,31 @@
+import sqlite3
+from datetime import datetime, timedelta
+
+DB_PATH = "/app/connector_queue.db"
+
+def clear_all_zombies():
+    print("🧹 Cleaning up Zombie Jobs (PROCESSING for too long)...")
+    # A job that is PROCESSING for more than 10 minutes is likely dead
+    threshold = (datetime.utcnow() - timedelta(minutes=10)).strftime('%Y-%m-%d %H:%M:%S')
+    
+    with sqlite3.connect(DB_PATH) as conn:
+        cursor = conn.cursor()
+        
+        # 1. Identify Zombies
+        cursor.execute("SELECT id, updated_at FROM jobs WHERE status = 'PROCESSING' AND updated_at < ?", (threshold,))
+        zombies = cursor.fetchall()
+        
+        if not zombies:
+            print("✅ No zombies found.")
+            return
+
+        print(f"🕵️ Found {len(zombies)} zombie jobs.")
+        for zid, updated in zombies:
+            print(f"   - Zombie ID {zid} (Last active: {updated})")
+
+        # 2. Kill them
+        cursor.execute("UPDATE jobs SET status = 'FAILED', error_msg = 'Zombie cleared: Process timed out' WHERE status = 'PROCESSING' AND updated_at < ?", (threshold,))
+        print(f"✅ Successfully cleared {cursor.rowcount} zombie(s).")
+
+if __name__ == "__main__":
+    clear_all_zombies()

ARCHIVE_legacy_scripts/debug_connector_status.py

@@ -0,0 +1,49 @@
+import sqlite3
+import json
+import os
+
+DB_PATH = "connector_queue.db"
+
+def inspect_queue():
+    if not os.path.exists(DB_PATH):
+        print(f"❌ Database not found at {DB_PATH}")
+        return
+
+    print(f"🔍 Inspecting Queue: {DB_PATH}")
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        conn.row_factory = sqlite3.Row
+        cursor = conn.cursor()
+        
+        # Get stats
+        cursor.execute("SELECT status, COUNT(*) FROM jobs GROUP BY status")
+        stats = dict(cursor.fetchall())
+        print(f"\n📊 Stats: {stats}")
+        
+        # Get recent jobs
+        print("\n📝 Last 10 Jobs:")
+        cursor.execute("SELECT id, event_type, status, error_msg, updated_at, payload FROM jobs ORDER BY updated_at DESC LIMIT 10")
+        rows = cursor.fetchall()
+        
+        for row in rows:
+            payload = json.loads(row['payload'])
+            # Try to identify entity
+            entity = "Unknown"
+            if "PrimaryKey" in payload: entity = f"ID {payload['PrimaryKey']}"
+            if "ContactId" in payload: entity = f"Contact {payload['ContactId']}"
+            
+            print(f" - Job #{row['id']} [{row['status']}] {row['event_type']} ({entity})")
+            print(f"   Updated: {row['updated_at']}")
+            if row['error_msg']:
+                print(f"   ❌ ERROR: {row['error_msg']}")
+            
+            # Print payload details relevant to syncing
+            if row['status'] == 'COMPLETED':
+                 pass # Maybe less interesting if success, but user says it didn't sync
+            
+        conn.close()
+    except Exception as e:
+        print(f"❌ Error reading DB: {e}")
+
+if __name__ == "__main__":
+    inspect_queue()

ARCHIVE_legacy_scripts/debug_paths.py

@@ -0,0 +1,13 @@
+import os
+static_path = "/frontend_static"
+print(f"Path {static_path} exists: {os.path.exists(static_path)}")
+if os.path.exists(static_path):
+    for root, dirs, files in os.walk(static_path):
+        for file in files:
+            print(os.path.join(root, file))
+else:
+    print("Listing /app instead:")
+    for root, dirs, files in os.walk("/app"):
+        if "node_modules" in root: continue
+        for file in files:
+            print(os.path.join(root, file))

ARCHIVE_legacy_scripts/debug_zombie.py

@@ -0,0 +1,16 @@
+import sqlite3
+import os
+
+DB_PATH = "/app/connector_queue.db"
+
+if __name__ == "__main__":
+    print(f"📊 Accessing database at {DB_PATH}")
+    print("📊 Listing last 20 jobs in database...")
+    with sqlite3.connect(DB_PATH) as conn:
+        conn.row_factory = sqlite3.Row
+        cursor = conn.cursor()
+        cursor.execute("SELECT id, status, event_type, updated_at FROM jobs ORDER BY id DESC LIMIT 20")
+        rows = cursor.fetchall()
+        for r in rows:
+            print(f"   - Job {r['id']}: {r['status']} ({r['event_type']}) - Updated: {r['updated_at']}")
+

ARCHIVE_legacy_scripts/fix_benni_data.py

@@ -0,0 +1,41 @@
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+import json
+
+# Setup DB
+DB_PATH = "sqlite:///companies_v3_fixed_2.db"
+engine = create_engine(DB_PATH)
+SessionLocal = sessionmaker(bind=engine)
+session = SessionLocal()
+
+from sqlalchemy import Column, Integer, String
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class Company(Base):
+    __tablename__ = "companies"
+    id = Column(Integer, primary_key=True)
+    street = Column(String)
+    zip_code = Column(String)
+
+def fix_benni():
+    company_id = 33
+    print(f"🔧 Fixing Address for Company ID {company_id}...")
+    
+    company = session.query(Company).filter_by(id=company_id).first()
+    if not company:
+        print("❌ Company not found.")
+        return
+
+    # Hardcoded from previous check_benni.py output to be safe/fast
+    # "street": "Eriagstraße 58", "zip": "85053"
+    
+    company.street = "Eriagstraße 58"
+    company.zip_code = "85053"
+    
+    session.commit()
+    print(f"✅ Database updated: Street='{company.street}', Zip='{company.zip_code}'")
+
+if __name__ == "__main__":
+    fix_benni()

ARCHIVE_legacy_scripts/fix_industry_units.py

@@ -0,0 +1,70 @@
+import sqlite3
+
+DB_PATH = "companies_v3_fixed_2.db"
+
+UNIT_MAPPING = {
+    "Logistics - Warehouse": "m²",
+    "Healthcare - Hospital": "Betten",
+    "Infrastructure - Transport": "Passagiere", 
+    "Leisure - Indoor Active": "m²",
+    "Retail - Food": "m²",
+    "Retail - Shopping Center": "m²",
+    "Hospitality - Gastronomy": "Sitzplätze",
+    "Leisure - Outdoor Park": "Besucher",
+    "Leisure - Wet & Spa": "Besucher",
+    "Infrastructure - Public": "Kapazität",
+    "Retail - Non-Food": "m²",
+    "Hospitality - Hotel": "Zimmer",
+    "Leisure - Entertainment": "Besucher",
+    "Healthcare - Care Home": "Plätze",
+    "Industry - Manufacturing": "Mitarbeiter", 
+    "Energy - Grid & Utilities": "Kunden",
+    "Leisure - Fitness": "Mitglieder",
+    "Corporate - Campus": "Mitarbeiter",
+    "Energy - Solar/Wind": "MWp", 
+    "Tech - Data Center": "Racks", 
+    "Automotive - Dealer": "Fahrzeuge",
+    "Infrastructure Parking": "Stellplätze",
+    "Reinigungsdienstleister": "Mitarbeiter",
+    "Infrastructure - Communities": "Einwohner"
+}
+
+def fix_units():
+    print(f"Connecting to {DB_PATH}...")
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    
+    try:
+        cursor.execute("SELECT id, name, scraper_search_term, metric_type FROM industries")
+        rows = cursor.fetchall()
+        
+        updated_count = 0
+        
+        for row in rows:
+            ind_id, name, current_term, m_type = row
+            
+            new_term = UNIT_MAPPING.get(name)
+            
+            # Fallback Logic
+            if not new_term:
+                if m_type in ["AREA_IN", "AREA_OUT"]:
+                    new_term = "m²"
+                else:
+                    new_term = "Anzahl" # Generic fallback
+            
+            if current_term != new_term:
+                print(f"Updating '{name}': '{current_term}' -> '{new_term}'")
+                cursor.execute("UPDATE industries SET scraper_search_term = ? WHERE id = ?", (new_term, ind_id))
+                updated_count += 1
+        
+        conn.commit()
+        print(f"\n✅ Updated {updated_count} industries with correct units.")
+        
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        conn.rollback()
+    finally:
+        conn.close()
+
+if __name__ == "__main__":
+    fix_units()

ARCHIVE_legacy_scripts/fix_mappings_v2.py

@@ -0,0 +1,23 @@
+import sqlite3
+
+def fix_mappings():
+    conn = sqlite3.connect('/app/companies_v3_fixed_2.db')
+    cursor = conn.cursor()
+    
+    # Neue Mappings für Geschäftsleitung und Verallgemeinerung
+    new_rules = [
+        ('%leitung%', 'Wirtschaftlicher Entscheider'),
+        ('%vorstand%', 'Wirtschaftlicher Entscheider'),
+        ('%geschäftsleitung%', 'Wirtschaftlicher Entscheider'),
+        ('%management%', 'Wirtschaftlicher Entscheider')
+    ]
+    
+    for pattern, role in new_rules:
+        cursor.execute("INSERT OR REPLACE INTO job_role_mappings (pattern, role, created_at) VALUES (?, ?, '2026-02-22T15:30:00')", (pattern, role))
+    
+    conn.commit()
+    conn.close()
+    print("Mappings updated for Geschäftsleitung, Vorstand, Management.")
+
+if __name__ == "__main__":
+    fix_mappings()

ARCHIVE_legacy_scripts/fix_silly_billy_data.py

@@ -0,0 +1,90 @@
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+import json
+import logging
+
+# Setup DB
+DB_PATH = "sqlite:///companies_v3_fixed_2.db"
+engine = create_engine(DB_PATH)
+SessionLocal = sessionmaker(bind=engine)
+session = SessionLocal()
+
+# Import Models (Simplified for script)
+from sqlalchemy import Column, Integer, String, Text, JSON
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class Company(Base):
+    __tablename__ = "companies"
+    id = Column(Integer, primary_key=True)
+    name = Column(String)
+    city = Column(String)
+    country = Column(String)
+    crm_vat = Column(String)
+    street = Column(String)
+    zip_code = Column(String)
+
+class EnrichmentData(Base):
+    __tablename__ = "enrichment_data"
+    id = Column(Integer, primary_key=True)
+    company_id = Column(Integer)
+    source_type = Column(String)
+    content = Column(JSON)
+
+def fix_data():
+    company_id = 32
+    print(f"🔧 Fixing Data for Company ID {company_id}...")
+    
+    company = session.query(Company).filter_by(id=company_id).first()
+    if not company:
+        print("❌ Company not found.")
+        return
+
+    enrichment = session.query(EnrichmentData).filter_by(
+        company_id=company_id, source_type="website_scrape"
+    ).first()
+    
+    if enrichment and enrichment.content:
+        imp = enrichment.content.get("impressum")
+        if imp:
+            print(f"📄 Found Impressum: {imp}")
+            
+            changed = False
+            if imp.get("city"):
+                company.city = imp.get("city")
+                changed = True
+                print(f"  -> Set City: {company.city}")
+                
+            if imp.get("vat_id"):
+                company.crm_vat = imp.get("vat_id")
+                changed = True
+                print(f"  -> Set VAT: {company.crm_vat}")
+            
+            if imp.get("country_code"):
+                company.country = imp.get("country_code")
+                changed = True
+                print(f"  -> Set Country: {company.country}")
+
+            if imp.get("street"):
+                company.street = imp.get("street")
+                changed = True
+                print(f"  -> Set Street: {company.street}")
+
+            if imp.get("zip"):
+                company.zip_code = imp.get("zip")
+                changed = True
+                print(f"  -> Set Zip: {company.zip_code}")
+
+            if changed:
+                session.commit()
+                print("✅ Database updated.")
+            else:
+                print("ℹ️ No changes needed.")
+        else:
+            print("⚠️ No impressum data in enrichment.")
+    else:
+        print("⚠️ No enrichment data found.")
+
+if __name__ == "__main__":
+    fix_data()

ARCHIVE_legacy_scripts/list_all_companies.py

@@ -0,0 +1,30 @@
+import sqlite3
+import os
+
+DB_PATH = "companies_v3_fixed_2.db"
+
+def list_companies():
+    if not os.path.exists(DB_PATH):
+        print(f"❌ Database not found at {DB_PATH}")
+        return
+
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"🔍 Listing companies in {DB_PATH}...")
+        cursor.execute("SELECT id, name, crm_id, city, crm_vat FROM companies ORDER BY id DESC LIMIT 20")
+        rows = cursor.fetchall()
+        
+        if not rows:
+            print("❌ No companies found")
+        else:
+            for row in rows:
+                print(f"  ID: {row[0]} | Name: {row[1]} | CRM ID: {row[2]} | City: {row[3]} | VAT: {row[4]}")
+            
+        conn.close()
+    except Exception as e:
+        print(f"❌ Error reading DB: {e}")
+
+if __name__ == "__main__":
+    list_companies()

ARCHIVE_legacy_scripts/list_industries.py

@@ -0,0 +1,18 @@
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), "company-explorer"))
+from backend.database import SessionLocal, Industry
+
+def list_industries():
+    db = SessionLocal()
+    try:
+        industries = db.query(Industry.name).all()
+        print("Available Industries:")
+        for (name,) in industries:
+            print(f"- {name}")
+    finally:
+        db.close()
+
+if __name__ == "__main__":
+    list_industries()

ARCHIVE_legacy_scripts/list_industries_db.py

@@ -0,0 +1,12 @@
+import sqlite3
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+conn = sqlite3.connect(DB_PATH)
+cursor = conn.cursor()
+
+cursor.execute("SELECT name FROM industries")
+industries = cursor.fetchall()
+print("Available Industries:")
+for ind in industries:
+    print(f"- {ind[0]}")
+conn.close()

ARCHIVE_legacy_scripts/market_db_manager.py

@@ -0,0 +1,120 @@
+import sqlite3
+import json
+import os
+import uuid
+from datetime import datetime
+
+DB_PATH = os.environ.get("DB_PATH", "/app/market_intelligence.db")
+
+def get_db_connection():
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    return conn
+
+def init_db():
+    conn = get_db_connection()
+    # Flexible schema: We store almost everything in a 'data' JSON column
+    conn.execute('''
+        CREATE TABLE IF NOT EXISTS projects (
+            id TEXT PRIMARY KEY,
+            name TEXT NOT NULL,
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            data JSON NOT NULL
+        )
+    ''')
+    conn.commit()
+    conn.close()
+
+def save_project(project_data):
+    """
+    Saves a project. If 'id' exists in data, updates it. Otherwise creates new.
+    """
+    conn = get_db_connection()
+    try:
+        project_id = project_data.get('id')
+        
+        # Extract a name for the list view (e.g. from companyName or referenceUrl)
+        # We assume the frontend passes a 'name' field, or we derive it.
+        name = project_data.get('name') or project_data.get('companyName') or "Untitled Project"
+        
+        if not project_id:
+            # Create New
+            project_id = str(uuid.uuid4())
+            project_data['id'] = project_id
+            
+            conn.execute(
+                'INSERT INTO projects (id, name, data) VALUES (?, ?, ?)',
+                (project_id, name, json.dumps(project_data))
+            )
+        else:
+            # Update Existing
+            conn.execute(
+                '''UPDATE projects 
+                   SET name = ?, data = ?, updated_at = CURRENT_TIMESTAMP 
+                   WHERE id = ?''',
+                (name, json.dumps(project_data), project_id)
+            )
+            
+        conn.commit()
+        return {"id": project_id, "status": "saved"}
+        
+    except Exception as e:
+        return {"error": str(e)}
+    finally:
+        conn.close()
+
+def get_all_projects():
+    conn = get_db_connection()
+    projects = conn.execute('SELECT id, name, created_at, updated_at FROM projects ORDER BY updated_at DESC').fetchall()
+    conn.close()
+    return [dict(ix) for ix in projects]
+
+def load_project(project_id):
+    conn = get_db_connection()
+    project = conn.execute('SELECT data FROM projects WHERE id = ?', (project_id,)).fetchone()
+    conn.close()
+    if project:
+        return json.loads(project['data'])
+    return None
+
+def delete_project(project_id):
+    conn = get_db_connection()
+    try:
+        conn.execute('DELETE FROM projects WHERE id = ?', (project_id,))
+        conn.commit()
+        return {"status": "deleted", "id": project_id}
+    except Exception as e:
+        return {"error": str(e)}
+    finally:
+        conn.close()
+
+if __name__ == "__main__":
+    import sys
+    # Simple CLI for Node.js bridge
+    # Usage: python market_db_manager.py [init|list|save|load|delete] [args...]
+    
+    mode = sys.argv[1]
+    
+    if mode == "init":
+        init_db()
+        print(json.dumps({"status": "initialized"}))
+        
+    elif mode == "list":
+        print(json.dumps(get_all_projects()))
+        
+    elif mode == "save":
+        # Data is passed as a JSON string file path to avoid command line length limits
+        data_file = sys.argv[2]
+        with open(data_file, 'r') as f:
+            data = json.load(f)
+        print(json.dumps(save_project(data)))
+        
+    elif mode == "load":
+        p_id = sys.argv[2]
+        result = load_project(p_id)
+        print(json.dumps(result if result else {"error": "Project not found"}))
+
+    elif mode == "delete":
+        p_id = sys.argv[2]
+        print(json.dumps(delete_project(p_id)))

ARCHIVE_legacy_scripts/migrate_opener_native.py

@@ -0,0 +1,29 @@
+import sqlite3
+import sys
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+
+def migrate():
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"Checking schema in {DB_PATH}...")
+        cursor.execute("PRAGMA table_info(companies)")
+        columns = [row[1] for row in cursor.fetchall()]
+        
+        if "ai_opener" in columns:
+            print("Column 'ai_opener' already exists. Skipping.")
+        else:
+            print("Adding column 'ai_opener' to 'companies' table...")
+            cursor.execute("ALTER TABLE companies ADD COLUMN ai_opener TEXT")
+            conn.commit()
+            print("✅ Migration successful.")
+            
+    except Exception as e:
+        print(f"❌ Migration failed: {e}")
+    finally:
+        if conn: conn.close()
+
+if __name__ == "__main__":
+    migrate()

ARCHIVE_legacy_scripts/migrate_opener_secondary.py

@@ -0,0 +1,29 @@
+import sqlite3
+import sys
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+
+def migrate():
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"Checking schema in {DB_PATH}...")
+        cursor.execute("PRAGMA table_info(companies)")
+        columns = [row[1] for row in cursor.fetchall()]
+        
+        if "ai_opener_secondary" in columns:
+            print("Column 'ai_opener_secondary' already exists. Skipping.")
+        else:
+            print("Adding column 'ai_opener_secondary' to 'companies' table...")
+            cursor.execute("ALTER TABLE companies ADD COLUMN ai_opener_secondary TEXT")
+            conn.commit()
+            print("✅ Migration successful.")
+            
+    except Exception as e:
+        print(f"❌ Migration failed: {e}")
+    finally:
+        if conn: conn.close()
+
+if __name__ == "__main__":
+    migrate()

ARCHIVE_legacy_scripts/migrate_personas_v2.py

@@ -0,0 +1,30 @@
+import sqlite3
+import os
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+
+def migrate_personas():
+    print(f"Adding new columns to 'personas' table in {DB_PATH}...")
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    
+    columns_to_add = [
+        ("description", "TEXT"),
+        ("convincing_arguments", "TEXT"),
+        ("typical_positions", "TEXT"),
+        ("kpis", "TEXT")
+    ]
+    
+    for col_name, col_type in columns_to_add:
+        try:
+            cursor.execute(f"ALTER TABLE personas ADD COLUMN {col_name} {col_type}")
+            print(f"  Added column: {col_name}")
+        except sqlite3.OperationalError:
+            print(f"  Column {col_name} already exists.")
+            
+    conn.commit()
+    conn.close()
+    print("Migration complete.")
+
+if __name__ == "__main__":
+    migrate_personas()

ARCHIVE_legacy_scripts/read_matrix_entry.py

@@ -0,0 +1,37 @@
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), "company-explorer"))
+from backend.database import SessionLocal, Industry, Persona, MarketingMatrix
+
+def read_specific_entry(industry_name: str, persona_name: str):
+    db = SessionLocal()
+    try:
+        entry = (
+            db.query(MarketingMatrix)
+            .join(Industry)
+            .join(Persona)
+            .filter(Industry.name == industry_name, Persona.name == persona_name)
+            .first()
+        )
+        
+        if not entry:
+            print(f"No entry found for {industry_name} and {persona_name}")
+            return
+
+        print("--- Generated Text ---")
+        print(f"Industry: {industry_name}")
+        print(f"Persona: {persona_name}")
+        print("\n[Intro]")
+        print(entry.intro)
+        print("\n[Social Proof]")
+        print(entry.social_proof)
+        print("----------------------")
+
+    finally:
+        db.close()
+
+if __name__ == "__main__":
+    read_specific_entry("Healthcare - Hospital", "Infrastruktur-Verantwortlicher")
+
+

ARCHIVE_legacy_scripts/standalone_importer.py

@@ -0,0 +1,92 @@
+import csv
+from collections import Counter
+import os
+import argparse
+from sqlalchemy import create_engine, Column, Integer, String, Boolean, DateTime
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.ext.declarative import declarative_base
+from datetime import datetime
+import logging
+
+# --- Standalone Configuration ---
+DATABASE_URL = "sqlite:////app/companies_v3_fixed_2.db"
+LOG_FILE = "/app/Log_from_docker/standalone_importer.log"
+
+# --- Logging Setup ---
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler(LOG_FILE),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
+
+# --- SQLAlchemy Models (simplified, only what's needed) ---
+Base = declarative_base()
+
+class RawJobTitle(Base):
+    __tablename__ = 'raw_job_titles'
+    id = Column(Integer, primary_key=True)
+    title = Column(String, unique=True, index=True)
+    count = Column(Integer, default=1)
+    source = Column(String, default="import")
+    is_mapped = Column(Boolean, default=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+# --- Database Connection ---
+engine = create_engine(DATABASE_URL)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+def import_job_titles_standalone(file_path: str):
+    db = SessionLocal()
+    try:
+        logger.info(f"Starting standalone import of job titles from {file_path}")
+        
+        job_title_counts = Counter()
+        total_rows = 0
+
+        with open(file_path, 'r', encoding='utf-8') as f:
+            reader = csv.reader(f)
+            for row in reader:
+                if row and row[0].strip():
+                    title = row[0].strip()
+                    job_title_counts[title] += 1
+                    total_rows += 1
+        
+        logger.info(f"Read {total_rows} total job title entries. Found {len(job_title_counts)} unique titles.")
+
+        added_count = 0
+        updated_count = 0
+
+        for title, count in job_title_counts.items():
+            existing_title = db.query(RawJobTitle).filter(RawJobTitle.title == title).first()
+            if existing_title:
+                if existing_title.count != count:
+                    existing_title.count = count
+                    updated_count += 1
+            else:
+                new_title = RawJobTitle(title=title, count=count, source="csv_import", is_mapped=False)
+                db.add(new_title)
+                added_count += 1
+        
+        db.commit()
+        logger.info(f"Standalone import complete. Added {added_count} new unique titles, updated {updated_count} existing titles.")
+
+    except Exception as e:
+        logger.error(f"Error during standalone job title import: {e}", exc_info=True)
+        db.rollback()
+    finally:
+        db.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Standalone script to import job titles from a CSV file.")
+    parser.add_argument("file_path", type=str, help="Path to the CSV file containing job titles.")
+    args = parser.parse_args()
+
+    # Ensure the log directory exists
+    os.makedirs(os.path.dirname(LOG_FILE), exist_ok=True)
+
+    import_job_titles_standalone(args.file_path)

ARCHIVE_legacy_scripts/test_api_logic.py

@@ -0,0 +1,22 @@
+import os
+import sys
+
+# Add the company-explorer directory to the Python path
+sys.path.append(os.path.abspath(os.path.join(os.getcwd(), 'company-explorer')))
+
+from backend.database import SessionLocal, MarketingMatrix, Industry, Persona
+from sqlalchemy.orm import joinedload
+
+db = SessionLocal()
+try:
+    query = db.query(MarketingMatrix).options(
+        joinedload(MarketingMatrix.industry),
+        joinedload(MarketingMatrix.persona)
+    )
+    entries = query.all()
+    print(f"Total entries: {len(entries)}")
+    for e in entries[:3]:
+        print(f"ID={e.id}, Industry={e.industry.name if e.industry else 'N/A'}, Persona={e.persona.name if e.persona else 'N/A'}")
+        print(f"  Subject: {e.subject}")
+finally:
+    db.close()

ARCHIVE_legacy_scripts/test_opener_api.py

@@ -0,0 +1,91 @@
+import requests
+import os
+import sys
+import time
+
+# Load credentials from .env
+# Simple manual parser to avoid dependency on python-dotenv
+def load_env(path):
+    if not os.path.exists(path):
+        print(f"Warning: .env file not found at {path}")
+        return
+    with open(path) as f:
+        for line in f:
+            if line.strip() and not line.startswith('#'):
+                key, val = line.strip().split('=', 1)
+                os.environ.setdefault(key, val)
+
+load_env('/app/.env')
+
+API_USER = os.getenv("API_USER", "admin")
+API_PASS = os.getenv("API_PASSWORD", "gemini")
+CE_URL = "http://127.0.0.1:8000" # Target the local container (assuming port 8000 is mapped)
+TEST_CONTACT_ID = 1 # Therme Erding
+
+def run_test():
+    print("🚀 STARTING API-LEVEL E2E TEXT GENERATION TEST\n")
+    
+    # --- Health Check ---
+    print("Waiting for Company Explorer API to be ready...")
+    for i in range(10):
+        try:
+            health_resp = requests.get(f"{CE_URL}/api/health", auth=(API_USER, API_PASS), timeout=2)
+            if health_resp.status_code == 200:
+                print("✅ API is ready.")
+                break
+        except requests.exceptions.RequestException:
+            pass
+        if i == 9:
+            print("❌ API not ready after 20 seconds. Aborting.")
+            return False
+        time.sleep(2)
+
+    scenarios = [
+        {"name": "Infrastructure Role", "job_title": "Facility Manager", "opener_field": "opener", "keyword": "Sicherheit"},
+        {"name": "Operational Role", "job_title": "Leiter Badbetrieb", "opener_field": "opener_secondary", "keyword": "Gäste"}
+    ]
+
+    all_passed = True
+    for s in scenarios:
+        print(f"--- Testing: {s['name']} ---")
+        endpoint = f"{CE_URL}/api/provision/superoffice-contact"
+        payload = {
+            "so_contact_id": TEST_CONTACT_ID,
+            "job_title": s['job_title']
+        }
+        
+        try:
+            resp = requests.post(endpoint, json=payload, auth=(API_USER, API_PASS))
+            resp.raise_for_status()
+            data = resp.json()
+            
+            # --- Assertions ---
+            opener = data.get('opener')
+            opener_sec = data.get('opener_secondary')
+
+            assert opener, "❌ FAIL: Primary opener is missing!"
+            print(f"   ✅ Primary Opener: '{opener}'")
+            
+            assert opener_sec, "❌ FAIL: Secondary opener is missing!"
+            print(f"   ✅ Secondary Opener: '{opener_sec}'")
+
+            target_opener_text = data.get(s['opener_field'])
+            assert s['keyword'].lower() in target_opener_text.lower(), f"❌ FAIL: Keyword '{s['keyword']}' not in '{s['opener_field']}'!"
+            print(f"   ✅ Keyword '{s['keyword']}' found in correct opener.")
+            
+            print(f"--- ✅ PASSED: {s['name']} ---\\n")
+
+        except Exception as e:
+            print(f"   ❌ TEST FAILED: {e}")
+            if hasattr(e, 'response') and e.response is not None:
+                print(f"      Response: {e.response.text}")
+            all_passed = False
+    
+    return all_passed
+
+if __name__ == "__main__":
+    if run_test():
+        print("🏁 All scenarios passed successfully!")
+    else:
+        print("🔥 Some scenarios failed.")
+        sys.exit(1)

ARCHIVE_legacy_scripts/test_provisioning_api.py

@@ -0,0 +1,12 @@
+import requests
+import json
+
+url = "http://company-explorer:8000/api/provision/superoffice-contact"
+payload = {"so_contact_id": 4}
+auth = ("admin", "gemini")
+
+try:
+    resp = requests.post(url, json=payload, auth=auth)
+    print(json.dumps(resp.json(), indent=2))
+except Exception as e:
+    print(f"Error: {e}")

ARCHIVE_legacy_scripts/trading_twins_tool.py

@@ -1,6 +1,16 @@
 import json
 import time
 import os
+import sys
+
+# Ensure we can import from lead-engine
+sys.path.append(os.path.join(os.path.dirname(__file__), 'lead-engine'))
+try:
+    from trading_twins_ingest import process_leads
+except ImportError:
+    print("Warning: Could not import trading_twins_ingest from lead-engine. Email ingestion disabled.")
+    process_leads = None
+
 from company_explorer_connector import handle_company_workflow
 
 def run_trading_twins_process(target_company_name: str):
@@ -46,6 +56,14 @@ def run_trading_twins_process(target_company_name: str):
     print(f"Trading Twins Analyse für {target_company_name} abgeschlossen.")
     print(f"{'='*50}\n")
 
+def run_email_ingest():
+    """Starts the automated email ingestion process for Tradingtwins leads."""
+    if process_leads:
+        print("\nStarting automated email ingestion via Microsoft Graph...")
+        process_leads()
+        print("Email ingestion completed.")
+    else:
+        print("Error: Email ingestion module not available.")
 
 if __name__ == "__main__":
     # Simulieren der Umgebungsvariablen für diesen Testlauf, falls nicht gesetzt
@@ -54,26 +72,28 @@ if __name__ == "__main__":
     if "COMPANY_EXPLORER_API_PASSWORD" not in os.environ:
         os.environ["COMPANY_EXPLORER_API_PASSWORD"] = "gemini"
 
-    # Testfall 1: Ein Unternehmen, das wahrscheinlich bereits existiert
+    print("Trading Twins Tool - Main Menu")
-    # Da 'Robo-Planet GmbH' bei den vorherigen Läufen erstellt wurde, sollte es jetzt gefunden werden.
+    print("1. Process specific company name")
-    run_trading_twins_process("Robo-Planet GmbH")
+    print("2. Ingest leads from Email (info@robo-planet.de)")
+    print("3. Run demo sequence (Robo-Planet, Erding, etc.)")
     
-    # Kurze Pause zwischen den Testläufen
+    choice = input("\nSelect option (1-3): ").strip()
-    time.sleep(5)
     
-    # Testfall 1b: Ein bekanntes, real existierendes Unternehmen
+    if choice == "1":
-    run_trading_twins_process("Klinikum Landkreis Erding")
+        name = input("Enter company name: ").strip()
-
+        if name:
-    # Kurze Pause zwischen den Testläufen
+            run_trading_twins_process(name)
-    time.sleep(5)
+    elif choice == "2":
-
+        run_email_ingest()
-    # Testfall 2: Ein neues, eindeutiges Unternehmen
+    elif choice == "3":
-    new_unique_company_name = f"Trading Twins New Target {int(time.time())}"
+        # Testfall 1: Ein Unternehmen, das wahrscheinlich bereits existiert
-    run_trading_twins_process(new_unique_company_name)
+        run_trading_twins_process("Robo-Planet GmbH")
-
+        time.sleep(2)
-    # Kurze Pause
+        # Testfall 1b: Ein bekanntes, real existierendes Unternehmen
-    time.sleep(5)
+        run_trading_twins_process("Klinikum Landkreis Erding")
-    
+        time.sleep(2)
-    # Testfall 3: Ein weiteres neues Unternehmen, um die Erstellung zu prüfen
+        # Testfall 2: Ein neues, eindeutiges Unternehmen
-    another_new_company_name = f"Another Demo Corp {int(time.time())}"
+        new_unique_company_name = f"Trading Twins New Target {int(time.time())}"
-    run_trading_twins_process(another_new_company_name)
+        run_trading_twins_process(new_unique_company_name)
+    else:
+        print("Invalid choice.")

ARCHIVE_legacy_scripts/trigger_resync.py

@@ -0,0 +1,25 @@
+import sqlite3
+import json
+import time
+
+DB_PATH = "connector_queue.db"
+
+def trigger_resync(contact_id):
+    print(f"🚀 Triggering manual resync for Contact {contact_id}...")
+    
+    payload = {
+        "Event": "contact.changed",
+        "PrimaryKey": contact_id,
+        "ContactId": contact_id,
+        "Changes": ["UserDefinedFields", "Name"] # Dummy changes to pass filters
+    }
+    
+    with sqlite3.connect(DB_PATH) as conn:
+        conn.execute(
+            "INSERT INTO jobs (event_type, payload, status) VALUES (?, ?, ?)",
+            ("contact.changed", json.dumps(payload), 'PENDING')
+        )
+    print("✅ Job added to queue.")
+
+if __name__ == "__main__":
+    trigger_resync(6) # Bennis Playland has CRM ID 6

ARCHIVE_legacy_scripts/verify_db.py

@@ -0,0 +1,13 @@
+import sqlite3
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+conn = sqlite3.connect(DB_PATH)
+cursor = conn.cursor()
+cursor.execute("SELECT name, description, convincing_arguments FROM personas")
+rows = cursor.fetchall()
+for row in rows:
+    print(f"Persona: {row[0]}")
+    print(f"  Description: {row[1][:100]}...")
+    print(f"  Convincing: {row[2][:100]}...")
+    print("-" * 20)
+conn.close()

ARCHIVE_vor_migration/Fotograf.de/README.md

@@ -0,0 +1,75 @@
+# Archivierte Fotograf.de Tools
+
+Dieses Verzeichnis (`ARCHIVE_vor_migration/Fotograf.de/`) enthält zwei archivierte Tools, die zuvor für die Interaktion mit `app.fotograf.de` und die Erstellung von Google Docs Teilnehmerlisten verwendet wurden.
+
+Beide Tools sind hier isoliert und dokumentiert, um eine spätere Wiederverwendung und Überarbeitung zu erleichtern.
+
+## 1. Fotograf.de Scraper
+
+**Verzeichnis:** `./scraper/`
+
+**Zweck:**
+Ein Python-basiertes Skript, das die Website `app.fotograf.de` automatisiert besucht, sich anmeldet und in zwei Modi Daten extrahiert:
+1.  **E-Mail-Liste erstellen:** Sammelt Kontaktdaten (Käufer, E-Mail, Kindnamen, Login-URLs) und speichert sie in einer CSV-Datei (`supermailer_fertige_liste.csv`).
+2.  **Statistik auswerten:** Erstellt eine Statistik-CSV-Datei (`job_statistik.csv`) über Album-Käufe.
+
+**Benötigte Dateien:**
+*   `./scraper/scrape_fotograf.py`: Das Hauptskript mit der gesamten Logik.
+*   `./scraper/fotograf_credentials.json`: **(Manuell zu erstellen!)** Diese Datei muss Ihre Login-Daten für `app.fotograf.de` im folgenden JSON-Format enthalten:
+    ```json
+    {
+      "PROFILNAME": {
+        "username": "IHR_BENUTZERNAME",
+        "password": "IHR_PASSWORT"
+      }
+    }
+    ```
+
+**Ausführung über Docker:**
+Das Tool wird in einer Docker-Umgebung ausgeführt, die Google Chrome und Selenium bereitstellt.
+
+1.  **Image bauen (einmalig oder bei Änderungen an Dockerfile/requirements.txt):**
+    Navigieren Sie zum Root-Verzeichnis des Hauptprojekts (`/app`) und verwenden Sie das dortige `Dockerfile.brancheneinstufung`:
+    ```bash
+    cd /app
+    docker build -f Dockerfile.brancheneinstufung -t fotograf-scraper .
+    ```
+    *(Hinweis: Das `Dockerfile.brancheneinstufung` verwendet die globale `requirements.txt` im Root-Verzeichnis, welche `selenium` enthält.)*
+
+2.  **Container starten und Skript ausführen:**
+    Vom Root-Verzeichnis des Hauptprojekts (`/app`) aus:
+    ```bash
+    cd /app
+    docker run -it --rm -v "$(pwd):/app" fotograf-scraper python3 /app/ARCHIVE_vor_migration/Fotograf.de/scraper/scrape_fotograf.py
+    ```
+    Das Skript fragt Sie interaktiv nach dem gewünschten Modus und der URL des Fotoauftrags.
+
+
+## 2. Google Docs Teilnehmerlisten-Generator
+
+**Verzeichnis:** `./list_generator/`
+
+**Zweck:**
+Ein Python-Skript, das CSV-Dateien einliest und daraus formatierte Teilnehmerlisten als neues Google Docs-Dokument im Google Drive erstellt. Das Tool ist interaktiv und fragt beim Start Details wie den Namen der Veranstaltung, den Einrichtungstyp und den Ausgabemodus ab.
+
+**Benötigte Dateien:**
+*   `./list_generator/list_generator.py`: Das Hauptskript mit der gesamten Logik.
+*   `./list_generator/Namensliste.csv`: **(Manuell zu erstellen!)** Eine CSV-Datei mit den Anmeldungen für Kindergärten/Schulen.
+*   `./list_generator/familien.csv`: **(Manuell zu erstellen!)** Eine CSV-Datei mit den Anmeldungen für Familien-Shootings.
+*   `./list_generator/service_account.json`: **(Manuell zu erstellen!)** Die JSON-Datei mit den Anmeldeinformationen für den Google Cloud Service Account. Diese Datei wird benötigt, um auf Google Docs und Google Drive zuzugreifen.
+
+**Ausführung:**
+Navigieren Sie in das Verzeichnis des Tools und starten Sie es mit Python:
+```bash
+cd /app/ARCHIVE_vor_migration/Fotograf.de/list_generator/
+python3 list_generator.py
+```
+*(Stellen Sie sicher, dass alle benötigten Python-Bibliotheken wie `google-api-python-client` etc. in Ihrer Umgebung installiert sind. Diese sind vermutlich über die globale `requirements.txt` im Root-Verzeichnis des Hauptprojekts verfügbar.)*
+
+## Wichtiger Hinweis zu Credentials (Sicherheit)
+
+Die Tools verwenden `fotograf_credentials.json` und `service_account.json` zur Authentifizierung. Diese Dateien enthalten sensitive Zugangsdaten und wurden **bewusst aus der Git-Historie entfernt** und nicht im Repository abgelegt.
+
+**Für die Wiederinbetriebnahme müssen diese Dateien manuell im jeweiligen Tool-Verzeichnis (`./scraper/` bzw. `./list_generator/`) erstellt und mit den korrekten Zugangsdaten befüllt werden.**
+
+**Priorität für die Überarbeitung:** Bei einer zukünftigen Überarbeitung dieser Tools ist es **zwingend erforderlich**, die Handhabung der Credentials zu verbessern. Statt fester JSON-Dateien sollten Umgebungsvariablen (`.env`) oder ein sicherer Secret Management Service verwendet werden, um die Sicherheitsstandards zu erhöhen.

ARCHIVE_vor_migration/Fotograf.de/list_generator/README.md

@@ -0,0 +1,22 @@
+# Google Docs Teilnehmerlisten-Generator (Archiviert)
+
+Dieses Verzeichnis enthält die archivierten Dateien für den "Google Docs Teilnehmerlisten-Generator".
+
+**Zweck:**
+Ein Python-Skript, das CSV-Dateien einliest und daraus formatierte Teilnehmerlisten als neues Google Docs-Dokument im Google Drive erstellt. Das Tool ist interaktiv und fragt beim Start Details wie den Namen der Veranstaltung ab.
+
+**Zugehörige Dateien in diesem Ordner:**
+*   `list_generator.py`: Das Hauptskript mit der gesamten Logik.
+
+**Manuell zu erstellende Dateien:**
+Diese Dateien werden vom Skript als Input benötigt und müssen im selben Verzeichnis liegen:
+*   `Namensliste.csv`: Eine CSV-Datei mit den Anmeldungen für Kindergärten/Schulen.
+*   `familien.csv`: Eine CSV-Datei mit den Anmeldungen für Familien-Shootings.
+*   `service_account.json`: Die JSON-Datei mit den Anmeldeinformationen für den Google Cloud Service Account, der die Berechtigung hat, auf Google Docs und Google Drive zuzugreifen.
+
+**Hinweis zur Ausführung:**
+Das Skript wird direkt mit Python ausgeführt, z.B.:
+```bash
+python3 list_generator.py
+```
+Stellen Sie sicher, dass alle benötigten Bibliotheken (wie `google-api-python-client`, `google-auth-httplib2`, `google-auth-oauthlib`) in Ihrer Python-Umgebung installiert sind. Diese sind vermutlich in der globalen `requirements.txt` im Root-Verzeichnis des Projekts enthalten.

ARCHIVE_vor_migration/Fotograf.de/scraper/README.md

@@ -0,0 +1,32 @@
+# Fotograf.de Scraper (Archiviert)
+
+Dieses Verzeichnis enthält die archivierten Dateien für den "Fotograf.de Scraper".
+
+**Zweck:**
+Ein Python-basiertes Tool, das die Website `app.fotograf.de` automatisiert besucht, sich anmeldet und in zwei Modi Daten extrahiert:
+1.  **E-Mail-Liste erstellen:** Sammelt Kontaktdaten und speichert sie in einer CSV-Datei (`supermailer_fertige_liste.csv`).
+2.  **Statistik auswerten:** Erstellt eine Statistik-CSV-Datei (`job_statistik.csv`).
+
+**Zugehörige Dateien in diesem Ordner:**
+*   `scrape_fotograf.py`: Das Hauptskript mit der gesamten Logik.
+
+**Manuell zu erstellende Dateien:**
+*   `fotograf_credentials.json`: Diese Datei wird vom Skript benötigt und muss die Login-Daten für `app.fotograf.de` im folgenden JSON-Format enthalten:
+    ```json
+    {
+      "PROFILNAME": {
+        "username": "IHR_BENUTZERNAME",
+        "password": "IHR_PASSWORT"
+      }
+    }
+    ```
+
+**Externe Abhängigkeiten (befinden sich im Hauptverzeichnis des Projekts):**
+*   **Dockerfile:** `Dockerfile.brancheneinstufung` wurde wahrscheinlich verwendet, um ein Docker-Image für dieses Tool zu erstellen. Es installiert Google Chrome und die notwendigen Python-Pakete.
+*   **Python-Abhängigkeiten:** Die globale `requirements.txt` im Root-Verzeichnis enthält `selenium` und andere benötigte Bibliotheken.
+
+**Beispielhafter `docker run`-Befehl:**
+1. Bauen Sie das Image (nur einmalig): `docker build -f Dockerfile.brancheneinstufung -t fotograf-scraper .`
+2. Führen Sie den Container aus: `docker run -it --rm -v "$(pwd):/app" fotograf-scraper python3 /app/ARCHIVE_vor_migration/Fotograf.de/scraper/scrape_fotograf.py`
+
+(Pfade müssen ggf. angepasst werden, je nachdem, von wo der Befehl ausgeführt wird.)

GEMINI.md

@@ -20,6 +20,41 @@ Dies ist in der Vergangenheit mehrfach passiert und hat zu massivem Datenverlust
 - **Git-Repository:** Dieses Projekt wird über ein Git-Repository verwaltet. Alle Änderungen am Code werden versioniert. Beachten Sie den Abschnitt "Git Workflow & Conventions" für unsere Arbeitsregeln.
     - **WICHTIG:** Der AI-Agent kann Änderungen committen, aber aus Sicherheitsgründen oft nicht `git push` ausführen. Bitte führen Sie `git push` manuell aus, wenn der Agent dies meldet.
 
+---
+## ‼️ Aktueller Projekt-Fokus (März 2026): Migration & Stabilisierung
+
+**Das System wurde am 07. März 2026 vollständig stabilisiert und für den Umzug auf die Ubuntu VM (`docker1`) vorbereitet.**
+
+Alle aktuellen Aufgaben für den Umzug sind hier zentralisiert:
+➡️ **[`RELOCATION.md`](./RELOCATION.md)**
+
+---
+
+## ✅ Current Status (March 7, 2026) - STABLE
+
+Das System läuft stabil auf der Synology-Entwicklungsumgebung.
+
+### 1. SuperOffice Connector (v2.1.1 - "Echo Shield")
+*   **Echo-Prävention (Härtung):** Der Worker (`worker.py`) identifiziert sich beim Start dynamisch (`/Associate/Me`) und ignoriert strikt alle Events, die vom eigenen User (z.B. ID 528) ausgelöst wurden.
+*   **Feld-Filter:** Änderungen werden nur verarbeitet, wenn relevante Felder (Name, URL, JobTitle) betroffen sind. Irrelevante Updates (z.B. `lastUpdated`) werden geskippt.
+*   **Webhook:** Registriert auf `https://floke-ai.duckdns.org/connector/webhook` mit Token-Validierung im Query-String.
+
+### 2. Company Explorer (v0.7.4)
+*   **Datenbank:** Schema repariert (`fix_missing_columns.py` ausgeführt). Fehlende Spalten (`street`, `zip_code`, `unsubscribe_token`) sind nun vorhanden.
+*   **Frontend:** Build-Pipeline repariert. PostCSS/Tailwind generieren jetzt wieder korrektes Styling.
+*   **Persistence:** Datenbank liegt sicher im Docker Volume `explorer_db_data`.
+
+### 3. Lead Engine (Trading Twins)
+*   **Integration:** In `docker-compose.yml` integriert und unter `/lead/` via Gateway erreichbar.
+*   **Persistence:** Nutzt Volume `lead_engine_data`.
+*   **Status:** UI läuft. E-Mail-Ingest via MS Graph benötigt noch Credentials.
+
+### 4. Infrastructure
+*   **Secrets:** Alle API-Keys (OpenAI, Gemini, SO, DuckDNS) sind zentral in der `.env` Datei.
+*   **DuckDNS:** Service läuft und aktualisiert die IP erfolgreich.
+
+---
+
 ## Git Workflow & Conventions
 
 ### Den Arbeitstag abschließen mit `#fertig`
@@ -37,6 +72,38 @@ Wenn Sie `#fertig` eingeben, führt der Agent folgende Schritte aus:
     - Der Status des Tasks in Notion wird auf "Done" (oder einen anderen passenden Status) gesetzt.
 4.  **Commit & Push:** Wenn Code-Änderungen vorhanden sind, wird ein Commit erstellt und ein `git push` interaktiv angefragt.
 
+### ⚠️ Troubleshooting: Git `push`/`pull` Fehler in Docker-Containern
+
+Gelegentlich kann es vorkommen, dass `git push` oder `git pull` Befehle aus dem `gemini-session` Docker-Container heraus mit Fehlern wie `Could not resolve host` oder `Failed to connect to <Gitea-Domain>` fehlschlagen, selbst wenn die externe Gitea-URL (z.B. `floke-gitea.duckdns.org`) im Host-System erreichbar ist. Dies liegt daran, dass der Docker-Container möglicherweise nicht dieselben DNS-Auflösungsmechanismen oder eine direkte Verbindung zur externen Adresse hat.
+
+**Problem:** Standard-DNS-Auflösung und externe Hostnamen schlagen innerhalb des Docker-Containers fehl.
+
+**Lösung:** Um eine robuste und direkte Verbindung zum Gitea-Container auf dem *selben Docker-Host* herzustellen, sollte die Git Remote URL auf die **lokale IP-Adresse des Docker-Hosts** und die **token-basierte Authentifizierung** umgestellt werden.
+
+**Schritte zur Konfiguration:**
+
+1.  **Lokale IP des Docker-Hosts ermitteln:**
+    *   Finden Sie die lokale IP-Adresse des Servers (z.B. Ihrer Diskstation), auf dem die Docker-Container laufen. Beispiel: `192.168.178.6`.
+2.  **Gitea-Token aus `.env` ermitteln:**
+    *   Finden Sie das Gitea-Token (das im Format `<Token>` in der `.env`-Datei oder in der vorherigen `git remote -v` Ausgabe zu finden ist). Beispiel: `318c736205934dd066b6bbcb1d732931eaa7c8c4`.
+3.  **Git Remote URL aktualisieren:**
+    *   Verwenden Sie den folgenden Befehl, um die Remote-URL zu aktualisieren. Ersetzen Sie `<Username>`, `<Token>` und `<Local-IP-Adresse>` durch Ihre Werte.
+    ```bash
+    git remote set-url origin http://<Username>:<Token>@<Local-IP-Adresse>:3000/Floke/Brancheneinstufung2.git
+    ```
+    *   **Beispiel (mit Ihren Daten):**
+    ```bash
+    git remote set-url origin http://Floke:318c736205934dd066b6bbcb1d732931eaa7c8c4@192.168.178.6:3000/Floke/Brancheneinstufung2.git
+    ```
+    *(Hinweis: Für die interne Docker-Kommunikation ist `http` anstelle von `https` oft ausreichend und kann Probleme mit SSL-Zertifikaten vermeiden.)*
+4.  **Verifizierung:**
+    *   Führen Sie `git fetch` aus, um die neue Konfiguration zu testen. Es sollte nun ohne Passwortabfrage funktionieren:
+    ```bash
+    git fetch
+    ```
+
+Diese Konfiguration gewährleistet eine stabile Git-Verbindung innerhalb Ihrer Docker-Umgebung.
+
 
 ## Project Overview
 
@@ -105,6 +172,17 @@ The system architecture has evolved from a CLI-based toolset to a modern web app
     *   **Problem:** Users didn't see when a background job finished.
     *   **Solution:** Implementing a polling mechanism (`setInterval`) tied to a `isProcessing` state is superior to static timeouts for long-running AI tasks.
 
+7.  **Hyper-Personalized Marketing Engine (v3.2) - "Deep Persona Injection":**
+    *   **Problem:** Marketing texts were too generic and didn't reflect the specific psychological or operative profile of the different target roles (e.g., CFO vs. Facility Manager).
+    *   **Solution (Deep Sync & Prompt Hardening):**
+        1.  **Extended Schema:** Added `description`, `convincing_arguments`, and `kpis` to the `Persona` database model to store richer profile data.
+        2.  **Notion Master Sync:** Updated the synchronization logic to pull these deep insights directly from the Notion "Personas / Roles" database.
+        3.  **Role-Centric Prompts:** The `MarketingMatrix` generator was re-engineered to inject the persona's "Mindset" and "KPIs" into the prompt.
+    *   **Example (Healthcare):**
+        - **Infrastructure Lead:** Focuses now on "IT Security", "DSGVO Compliance", and "WLAN integration".
+        - **Economic Buyer (CFO):** Focuses on "ROI Amortization", "Reduction of Overtime", and "Flexible Financing (RaaS)".
+    *   **Verification:** Verified that the transition from a company-specific **Opener** (e.g., observing staff shortages at Klinikum Erding) to the **Role-specific Intro** (e.g., pitching transport robots to reduce walking distances for nursing directors) is seamless and logical.
+
 ## Metric Parser - Regression Tests
 To ensure the stability and accuracy of the metric extraction logic, a dedicated test suite (`/company-explorer/backend/tests/test_metric_parser.py`) has been created. It covers the following critical, real-world bug fixes:
 
@@ -122,8 +200,185 @@ To ensure the stability and accuracy of the metric extraction logic, a dedicated
 
 These tests are crucial for preventing regressions as the parser logic evolves.
 
-## Next Steps
+## Notion Maintenance & Data Sync
-*   **Marketing Automation:** Implement the actual sending logic (or export) based on the contact status.
+
-*   **Job Role Mapping Engine:** Connect the configured patterns to the contact import/creation process to auto-assign roles.
+Since the "Golden Record" for Industry Verticals (Pains, Gains, Products) resides in Notion, specific tools are available to read and sync this data.
-*   **Industry Classification Engine:** Connect the configured industries to the AI Analysis prompt to enforce the "Strict Mode" mapping.
+
-*   **Export:** Generate Excel/CSV enriched reports (already partially implemented via JSON export).
+**Location:** `/app/company-explorer/backend/scripts/notion_maintenance/`
+
+**Prerequisites:**
+- Ensure `.env` is loaded with `NOTION_API_KEY` and correct DB IDs.
+
+**Key Scripts:**
+
+1.  **`check_relations.py` (Reader - Deep):**
+    -   **Purpose:** Reads Verticals and resolves linked Product Categories (Relation IDs -> Names). Essential for verifying the "Primary/Secondary Product" logic.
+    -   **Usage:** `python3 check_relations.py`
+
+2.  **`update_notion_full.py` (Writer - Batch):**
+    -   **Purpose:** Batch updates Pains and Gains for multiple verticals. Use this as a template when refining the messaging strategy.
+    -   **Usage:** Edit the dictionary in the script, then run `python3 update_notion_full.py`.
+
+3.  **`list_notion_structure.py` (Schema Discovery):**
+    -   **Purpose:** Lists all property keys and page titles. Use this to debug schema changes (e.g. if a column was renamed).
+        - **Usage:** `python3 list_notion_structure.py`
+    
+    ## Next Steps (Updated Feb 27, 2026)
+    
+    ***HINWEIS:*** *Dieser Abschnitt ist veraltet. Die aktuellen nächsten Schritte beziehen sich auf die Migrations-Vorbereitung und sind in der Datei [`RELOCATION.md`](./RELOCATION.md) dokumentiert.*
+
+    *   **Notion Content:** Finalize "Pains" and "Gains" for all 25 verticals in the Notion master database.
+    *   **Intelligence:** Run `generate_matrix.py` in the Company Explorer backend to populate the matrix for all new English vertical names.
+    *   **Automation:** Register the production webhook (requires `admin-webhooks` rights) to enable real-time CRM sync without manual job injection.
+    *   **Execution:** Connect the "Sending Engine" (the actual email dispatch logic) to the SuperOffice fields.
+    *   **Monitoring:** Monitor the 'Atomic PATCH' logs in production for any 400 errors regarding field length or specific character sets.
+
+    
+    ## Company Explorer Access & Debugging
+    
+    The **Company Explorer** is the central intelligence engine.
+    
+    **Core Paths:**
+    *   **Database:** `/app/companies_v3_fixed_2.db` (SQLite)
+    *   **Backend Code:** `/app/company-explorer/backend/`
+    *   **Logs:** `/app/logs_debug/company_explorer_debug.log`
+    
+    **Accessing Data:**
+    To inspect live data without starting the full stack, use `sqlite3` directly or the helper scripts (if environment permits).
+    
+    *   **Direct SQL:** `sqlite3 /app/companies_v3_fixed_2.db "SELECT * FROM companies WHERE name LIKE '%Firma%';" `
+    *   **Python (requires env):** The app runs in a Docker container. When debugging from outside (CLI agent), Python dependencies like `sqlalchemy` might be missing in the global scope. Prefer `sqlite3` for quick checks.
+    
+    **Key Endpoints (Internal API :8000):**
+    *   `POST /api/provision/superoffice-contact`: Triggers the text generation logic.
+    *   `GET /api/companies/{id}`: Full company profile including enrichment data.
+    
+    **Troubleshooting:**
+    *   **"BaseModel" Error:** Usually a mix-up between Pydantic and SQLAlchemy `Base`. Check imports in `database.py`.
+    *   **Missing Dependencies:** The CLI agent runs in `/app` but not necessarily inside the container's venv. Use standard tools (`grep`, `sqlite3`) where possible.
+
+---
+
+## Critical Debugging Session (Feb 21, 2026) - Re-Stabilizing the Analysis Engine
+
+A critical session was required to fix a series of cascading failures in the `ClassificationService`. The key takeaways are documented here to prevent future issues.
+
+1.  **The "Phantom" `NameError`:**
+    *   **Symptom:** The application crashed with a `NameError: name 'joinedload' is not defined`, even though the import was correctly added to `classification.py`.
+    *   **Root Cause:** The `uvicorn` server's hot-reload mechanism within the Docker container did not reliably pick up file changes made from outside the container. A simple `docker-compose restart` was insufficient to clear the process's cached state.
+    *   **Solution:** After any significant code change, especially to imports or core logic, a forced-recreation of the container is **mandatory**.
+        ```bash
+        # Correct Way to Apply Changes:
+        docker-compose up -d --build --force-recreate company-explorer
+        ```
+
+2.  **The "Invisible" Logs:**
+    *   **Symptom:** No debug logs were being written, making it impossible to trace the execution flow.
+    *   **Root Cause:** The `LOG_DIR` path in `/company-explorer/backend/config.py` was misconfigured (`/app/logs_debug`) and did not point to the actual, historical log directory (`/app/Log_from_docker`).
+    *   **Solution:** Configuration paths must be treated as absolute and verified. Correcting the `LOG_DIR` path immediately resolved the issue.
+
+3.  **Inefficient Debugging Loop:**
+    *   **Symptom:** The cycle of triggering a background job via API, waiting, and then manually checking logs was slow and inefficient.
+    *   **Root Cause:** Lack of a tool to test the core application logic in isolation.
+    *   **Solution:** The creation of a dedicated, interactive test script (`/company-explorer/backend/scripts/debug_single_company.py`). This script allows running the entire analysis for a single company in the foreground, providing immediate and detailed feedback. This pattern is invaluable for complex, multi-step processes and should be a standard for future development.
+## Production Migration & Multi-Campaign Support (Feb 27, 2026)
+
+The system has been fully migrated to the SuperOffice production environment (`online3.superoffice.com`, tenant `Cust26720`). 
+
+### 1. Final UDF Mappings (Production)
+These ProgIDs are verified and active for the production tenant:
+
+| Field Purpose | Entity | ProgID | Notes |
+| :--- | :--- | :--- | :--- |
+| **MA Subject** | Person | `SuperOffice:19` | |
+| **MA Intro** | Person | `SuperOffice:20` | |
+| **MA Social Proof** | Person | `SuperOffice:21` | |
+| **MA Unsubscribe** | Person | `SuperOffice:22` | URL format |
+| **MA Campaign** | Person | `SuperOffice:23` | List field (uses `:DisplayText`) |
+| **Vertical** | Contact | `SuperOffice:83` | List field (mapped via JSON) |
+| **AI Summary** | Contact | `SuperOffice:84` | Truncated to 132 chars |
+| **AI Last Update** | Contact | `SuperOffice:85` | Format: `[D:MM/DD/YYYY HH:MM:SS]` |
+| **Opener Primary** | Contact | `SuperOffice:86` | |
+| **Opener Secondary**| Contact | `SuperOffice:87` | |
+| **Last Outreach** | Contact | `SuperOffice:88` | |
+
+### 2. Vertical ID Mapping (Production)
+The full list of 25 verticals with their internal SuperOffice IDs (List `udlist331`):
+`Automotive - Dealer: 1613, Corporate - Campus: 1614, Energy - Grid & Utilities: 1615, Energy - Solar/Wind: 1616, Healthcare - Care Home: 1617, Healthcare - Hospital: 1618, Hospitality - Gastronomy: 1619, Hospitality - Hotel: 1620, Industry - Manufacturing: 1621, Infrastructure - Communities: 1622, Infrastructure - Public: 1623, Infrastructure - Transport: 1624, Infrastructure - Parking: 1625, Leisure - Entertainment: 1626, Leisure - Fitness: 1627, Leisure - Indoor Active: 1628, Leisure - Outdoor Park: 1629, Leisure - Wet & Spa: 1630, Logistics - Warehouse: 1631, Others: 1632, Reinigungsdienstleister: 1633, Retail - Food: 1634, Retail - Non-Food: 1635, Retail - Shopping Center: 1636, Tech - Data Center: 1637`.
+
+### 3. Technical Lessons Learned (SO REST API)
+
+1.  **Atomic PATCH (Stability):** Bundling all contact updates into a single `PATCH` request to the `/Contact/{id}` endpoint is far more stable than sequential UDF updates. If one field fails (e.g. invalid property), the whole transaction might roll back or partially fail—proactive validation is key.
+2.  **Website Sync (`Urls` Array):** Updating the website via REST requires manipulating the `Urls` array property. Simple field assignment to `UrlAddress` fails during `PATCH`.
+    *   *Correct Format:* `"Urls": [{"Value": "https://example.com", "Description": "AI Discovered"}]`.
+3.  **List Resolution (`:DisplayText`):** To get the clean string value of a list field (like Campaign Name) without extra API calls, use the pseudo-field `ProgID:DisplayText` in the `$select` parameter.
+4.  **Field Length Limits:** Standard SuperOffice text UDFs are limited to approx. 140-254 characters. AI-generated summaries must be truncated (e.g. 132 chars) to avoid 400 Bad Request errors.
+5.  **Docker `env_file` Importance:** For production, mapping individual variables in `docker-compose.yml` is error-prone. Using `env_file: .env` ensures all services stay synchronized with the latest UDF IDs and mappings.
+6.  **Production URL Schema:** The production API is strictly hosted on `online3.superoffice.com` (for this tenant), while OAuth remains at `online.superoffice.com`.
+
+### 4. Campaign Trigger Logic
+The `worker.py` (v1.8) now extracts the `campaign_tag` from `SuperOffice:23:DisplayText`. This tag is passed to the Company Explorer's provisioning API. If a matching entry exists in the `MarketingMatrix` for that tag, specific texts are used; otherwise, it falls back to the "standard" Kaltakquise texts.
+
+### 5. SuperOffice Authentication (Critical Update Feb 28, 2026)
+
+**Problem:** Authentication failures ("Invalid refresh token" or "Invalid client_id") occurred because standard `load_dotenv()` did not override stale environment variables present in the shell process.
+
+**Solution:** Always use `load_dotenv(override=True)` in Python scripts to force loading the actual values from the `.env` file.
+
+**Correct Authentication Pattern (Python):**
+```python
+from dotenv import load_dotenv
+import os
+
+# CRITICAL: override=True ensures we read from .env even if env vars are already set
+load_dotenv(override=True)
+
+client_id = os.getenv("SO_CLIENT_ID")
+# ...
+```
+
+**Known Working Config (Production):**
+*   **Environment:** `online3`
+*   **Tenant:** `Cust26720`
+*   **Token Logic:** The `AuthHandler` implementation in `health_check_so.py` is the reference standard. Avoid using legacy `superoffice_client.py` without verifying it uses `override=True`.
+
+### 6. Sales & Opportunities (Roboplanet Specifics)
+
+When creating sales via API, specific constraints apply due to the shared tenant with Wackler:
+
+*   **SaleTypeId:** MUST be **14** (`GE:"Roboplanet Verkauf";`) to ensure the sale is assigned to the correct business unit.
+    *   *Alternative:* ID 16 (`GE:"Roboplanet Teststellung";`) for trials.
+*   **Mandatory Fields:**
+    *   `Saledate` (Estimated Date): Must be provided in ISO format (e.g., `YYYY-MM-DDTHH:MM:SSZ`).
+    *   `Person`: Highly recommended linking to a specific person, not just the company.
+*   **Context:** Avoid creating sales on the parent company "Wackler Service Group" (ID 3). Always target the specific lead company.
+
+### Analyse der SuperOffice `Sale`-Entität (März 2026)
+
+- **Ziel:** Erstellung eines Reports, der abbildet, welche Kunden welche Produkte angeboten bekommen oder gekauft haben. Die initiale Vermutung war, dass Produktinformationen oft als Freitext-Einträge und nicht über den offiziellen Produktkatalog erfasst werden.
+- **Problem:** Die Untersuchung der Datenstruktur zeigte, dass die API-Endpunkte zur Abfrage von `Quote`-Objekten (Angeboten) und `QuoteLines` (Angebotspositionen) über `Sale`-, `Contact`- oder `Project`-Beziehungen hinweg nicht zuverlässig funktionierten. Viele Abfragen resultierten in `500 Internal Server Errors` oder leeren Datenmengen, was eine direkte Verknüpfung von Verkauf zu Produkt unmöglich machte.
+- **Kern-Erkenntnis (Datenstruktur):**
+    1.  **Freitext statt strukturierter Daten:** Die Analyse eines konkreten `Sale`-Objekts (ID `342243`) bestätigte die ursprüngliche Hypothese. Produktinformationen (z.B. `2xOmnie CD-01 mit Nachlass`) werden direkt in das `Heading`-Feld (Betreff) des `Sale`-Objekts als Freitext eingetragen. Es existieren oft keine verknüpften `Quote`- oder `QuoteLine`-Entitäten.
+    2.  **Datenqualität bei Verknüpfungen:** Eine signifikante Anzahl von `Sale`-Objekten im System weist keine Verknüpfung zu einem `Contact`-Objekt auf (`Contact: null`). Dies erschwert die automatische Zuordnung von Verkäufen zu Kunden erheblich.
+- **Nächster Schritt / Lösungsweg:** Ein Skript (`/app/connector-superoffice/generate_customer_product_report.py`) wurde entwickelt, das diese Probleme adressiert. Es fragt gezielt nur `Sale`-Objekte ab, die eine gültige `Contact`-Verknüpfung besitzen (`$filter=Contact ne null`). Anschließend extrahiert es den Kundennamen und das `Heading`-Feld des Verkaufs und durchsucht letzteres nach vordefinierten Produkt-Schlüsselwörtern. Die Ergebnisse werden für die manuelle Analyse in einer CSV-Datei (`product_report.csv`) gespeichert. Dieser Ansatz ist der einzig verlässliche Weg, um die gewünschten Informationen aus dem System zu extrahieren.
+
+### 7. Service & Tickets (Anfragen)
+
+SuperOffice Tickets represent the support and request system. Like Sales, they are organized to allow separation between Roboplanet and Wackler.
+
+*   **Entity Name:** `ticket`
+*   **Roboplanet Specific Categories (CategoryId):**
+    *   **ID 46:** `GE:"Lead Roboplanet";`
+    *   **ID 47:** `GE:"Vertriebspartner Roboplanet";`
+    *   **ID 48:** `GE:"Weitergabe Roboplanet";`
+    *   **Hierarchical:** `Roboplanet/Support` (often used for technical issues).
+*   **Key Fields:**
+    *   `ticketId`: Internal ID.
+    *   `title`: The subject of the request.
+    *   `contactId` / `personId`: Links to company and contact person.
+    *   `ticketStatusId`: 1 (Unbearbeitet), 2 (In Arbeit), 3 (Bearbeitet).
+    *   `ownedBy`: Often "ROBO" for Roboplanet staff.
+*   **Cross-Links:** Tickets can be linked to `saleId` (to track support during a sale) or `projectId`.
+
+---
+This is the core logic used to generate the company-specific opener.

Gruppenlisten_Output.txt

@@ -1,385 +0,0 @@
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
-----------------------------------------------------------------
-Bauer                    Emilie Christiana        Bienengruppe
-Eberl                    Maximilian               Bienengruppe
-Eckinger                 Charly                   Bienengruppe
-Ehrensberger             Theo                     Bienengruppe
-Gugic                    Vanessa                  Bienengruppe
-Hermansdorfer            Marina                   Bienengruppe
-Kaiser                   Theresa                  Bienengruppe
-Mehringer                Quirin                   Bienengruppe
-Müller                   Helene                   Bienengruppe
-Nowak                    Juna                     Bienengruppe
-Root                     Jonas                    Bienengruppe
-Tress                    Anna                     Bienengruppe
-Vass                     Marcell                  Bienengruppe
-Vilgertshofer            Clara                    Bienengruppe
-Wamprechtshammer         Marie                    Bienengruppe
-
-15 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
----------------------------------------------------------------
-Arndt                    Alina                    Eulengruppe
-Ben Hiba                 Elias                    Eulengruppe
-Benke                    Bastian                  Eulengruppe
-Berg                     Tamara                   Eulengruppe
-Berndt                   Mariella                 Eulengruppe
-Brose                    Valentina                Eulengruppe
-Drexler                  Lukas                    Eulengruppe
-Eisenhofer               Kilian                   Eulengruppe
-Etterer                  Juna                     Eulengruppe
-Fink                     Sophia                   Eulengruppe
-Fleissner                Sebastian                Eulengruppe
-Gebhardt                 Anton                    Eulengruppe
-Götz                     Ludwig                   Eulengruppe
-Josef                    Maximilian               Eulengruppe
-Klimaschewski            Ben                      Eulengruppe
-Kroh                     Louis                    Eulengruppe
-Lavalie                  Alvin                    Eulengruppe
-Michalik                 Fabian                   Eulengruppe
-Multhammer               Vincent                  Eulengruppe
-Multhammer               Vincent Ludwig           Eulengruppe
-Reck                     Sophia                   Eulengruppe
-Richter                  Oskar                    Eulengruppe
-Rocha Elias              Lorenzo                  Eulengruppe
-Rudolph                  Anna                     Eulengruppe
-Ternavska                Kira                     Eulengruppe
-Varadi                   Amelie                   Eulengruppe
-Varadi                   Clara                    Eulengruppe
-Zerr                     Oskar                    Eulengruppe
-
-28 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
------------------------------------------------------------------
-Bauer                    Luka Maximilian Željko   Fröschegruppe
-Birladeanu               Ianis Andrei             Fröschegruppe
-Eberhardt                Valentin                 Fröschegruppe
-Gryczuk                  Oliver                   Fröschegruppe
-Kelly                    Ferdinand                Fröschegruppe
-Koburger                 Leonie                   Fröschegruppe
-Kressirer                Josephine                Fröschegruppe
-Kunz                     Sophie                   Fröschegruppe
-Käsmeier                 Xaver                    Fröschegruppe
-Magin                    Lilly                    Fröschegruppe
-Mert                     Lukas                    Fröschegruppe
-Schmitz                  Juna                     Fröschegruppe
-Schütz                   Mirjam                   Fröschegruppe
-Wamprechtshammer         Moritz                   Fröschegruppe
-Wimmer                   Annika                   Fröschegruppe
-Zerr                     Paul                     Fröschegruppe
-
-16 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
----------------------------------------------------------------
-Albano                   Felicitas                Hasengruppe
-Ben Hiba                 Yassin                   Hasengruppe
-Daiser                   Greta                    Hasengruppe
-Fischer                  Alexander                Hasengruppe
-Gerlsbeck                Lena                     Hasengruppe
-Gottwalt                 Greta                    Hasengruppe
-Gruber                   Sophia                   Hasengruppe
-Hadzic                   Anelia                   Hasengruppe
-Kundt                    Patrick                  Hasengruppe
-Lechner                  Maximilian               Hasengruppe
-Numberger                Benedikt                 Hasengruppe
-Scherber                 Lukas                    Hasengruppe
-Tress                    Leo                      Hasengruppe
-Türe                     Emine                    Hasengruppe
-Wagner                   Isabell                  Hasengruppe
-
-15 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
------------------------------------------------------------------
-Brandl                   Mila                     Koboldegruppe
-Engelking                Melina                   Koboldegruppe
-Fink                     Anna                     Koboldegruppe
-Flügel                   Antonia                  Koboldegruppe
-Heindl                   Louis                    Koboldegruppe
-Kundt                    Philip                   Koboldegruppe
-Lechner                  Ben                      Koboldegruppe
-Müller                   Johanna                  Koboldegruppe
-Schütz                   Rafaela                  Koboldegruppe
-Seibold                  Marie                    Koboldegruppe
-Seibold                  Sophia                   Koboldegruppe
-Wenninger                Rosa                     Koboldegruppe
-
-12 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
----------------------------------------------------------------------
-Brose                    Marie                    Marienkäfergruppe
-Burghardt                Fanny                    Marienkäfergruppe
-Daberger                 Tobias                   Marienkäfergruppe
-Grobler                  Emelia                   Marienkäfergruppe
-Hamdard                  Faria                    Marienkäfergruppe
-Hermansdorfer            Vincent                  Marienkäfergruppe
-Huber                    Helena                   Marienkäfergruppe
-Klaric                   Mia                      Marienkäfergruppe
-Kraus                    Viktoria                 Marienkäfergruppe
-Kroh                     Maximilian               Marienkäfergruppe
-Schuster                 Korbinian                Marienkäfergruppe
-Wenninger                Valentin                 Marienkäfergruppe
-Zehetmaier               Emma                     Marienkäfergruppe
-
-13 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
--------------------------------------------------------------------
-Arndt                    Felix                    Maulwürfegruppe
-Beck                     Josef                    Maulwürfegruppe
-Ehrensberger             Zeno                     Maulwürfegruppe
-Eisenhofer               Antonia                  Maulwürfegruppe
-Farin                    Constantin               Maulwürfegruppe
-Fink                     Xaver                    Maulwürfegruppe
-Fischer                  Joshua                   Maulwürfegruppe
-Flügel                   Johann                   Maulwürfegruppe
-Hadzic                   Aylin                    Maulwürfegruppe
-Kressirer                Simon                    Maulwürfegruppe
-Kroh                     Liana                    Maulwürfegruppe
-Kugler                   Milena Vaiana            Maulwürfegruppe
-Magin                    Lotte                    Maulwürfegruppe
-Michalik                 Sarah                    Maulwürfegruppe
-Rocha Elias              Matteo                   Maulwürfegruppe
-Schleier                 Valentin                 Maulwürfegruppe
-Sedlmeir                 Julia                    Maulwürfegruppe
-Suszczewicz              Adam                     Maulwürfegruppe
-Tratnik                  Maja                     Maulwürfegruppe
-Wagner                   Mariella                 Maulwürfegruppe
-Winkler                  Clara                    Maulwürfegruppe
-
-21 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
-------------------------------------------------------------------------
-Birladeanu               Dima Matei               Schmetterlingegruppe
-Eichner                  Hanna                    Schmetterlingegruppe
-Gruber                   Johanna                  Schmetterlingegruppe
-Gschwendtner             Theresa                  Schmetterlingegruppe
-Haberthaler              Moritz                   Schmetterlingegruppe
-Hamdard                  Avesta                   Schmetterlingegruppe
-Josef                    Konstantin               Schmetterlingegruppe
-Mikołajczak              Gabriela                 Schmetterlingegruppe
-Pfaus                    Leo                      Schmetterlingegruppe
-Sulejmanovic             Elna                     Schmetterlingegruppe
-
-10 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
------------------------------------------------------------------
-Daiser                   Nora                     Wichtelgruppe
-Fabri                    Jule                     Wichtelgruppe
-Fink                     Leonie                   Wichtelgruppe
-Gojanaj                  Simon                    Wichtelgruppe
-Haberthaler              Anton                    Wichtelgruppe
-Hoffmann                 Eva                      Wichtelgruppe
-Hoffmann                 Eva Charlotte            Wichtelgruppe
-Koburger                 Paula                    Wichtelgruppe
-Mair                     Magdalena                Wichtelgruppe
-Sammer                   Emilie                   Wichtelgruppe
-
-10 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
----------------------------------------------------------------
-Berndt                   Milena                   Wölfegruppe
-Brose                    Sophia                   Wölfegruppe
-Dressel                  Lucy Jolie               Wölfegruppe
-Etterer                  Nele                     Wölfegruppe
-Fischer                  Benjamin                 Wölfegruppe
-Götz                     Georg                    Wölfegruppe
-Hermansdorfer            Ludwig                   Wölfegruppe
-Klimaschewski            Leon                     Wölfegruppe
-Magin                    Frederik                 Wölfegruppe
-Müller                   Luisa                    Wölfegruppe
-Niedermeier              Lukas                    Wölfegruppe
-
-11 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867
-
---- SEITENWECHSEL / PAGE BREAK ---
-
-Kinderhaus St. Martin Neuching			Kinderfotos Erding
-02. - 05.06.2025
-
-Nachname                 Vorname                  Gruppe
-------------------------------------------------------------------
-Baur                     Leon Malouis             Zwergerlgruppe
-Grobler                  Eloise                   Zwergerlgruppe
-Hagn                     Maximilian               Zwergerlgruppe
-Hren                     Jan                      Zwergerlgruppe
-Kugler                   Amaya Marina             Zwergerlgruppe
-Schmidt                  Emilie                   Zwergerlgruppe
-
-6 angemeldete Kinder
-
-Dies ist die Liste der bereits angemeldeten Kinder. Bitte die Eltern der noch fehlenden
-Kinder an die Anmeldung erinnern.
-
-Stand 26.05.2025 20:04 Uhr
-
-
-Kinderfotos Erding
-Gartenstr. 10 85445 Oberding
-www.kinderfotos-erding.de
-08122-8470867

HA_configuration.yaml

@@ -1,667 +0,0 @@
-# Loads default set of integrations. Do not remove.
-default_config:
-
-# Load frontend themes from the themes folder
-frontend:
-  themes: !include_dir_merge_named themes
-
-# Text to speech
-tts:
-  - platform: google_translate
-
-automation: !include automations.yaml
-script: !include scripts.yaml
-scene: !include scenes.yaml
-
-#Anleitung: https://book.cryd.de/books/projekte/page/hausverbrauch-strom-messen-incl-dummy-sensoren
-
-utility_meter:
-  daily_upload_volume:
-    source: sensor.fritzbox_upload_volumen
-    cycle: daily
-  daily_download_volume:
-    source: sensor.fritzbox_download_volumen
-    cycle: daily
-  taeglicher_stromverbrauch:
-    source: sensor.stromzahler_energieverbrauch
-    cycle: daily
-  taegliche_einspeisung:
-    source: sensor.stromzahler_energieeinspeisung
-    cycle: daily
-
-input_boolean:
-  manual_trigger:
-    name: Manual Trigger
-    initial: off
-
-influxdb:
-  host: 127.0.0.1
-  #host: a0d7b954-influxdb
-  port: 8086
-  database: homeassistant
-  username: !secret influxdb_user
-  password: !secret influxdb_pw
-  max_retries: 3
-  default_measurement: state
-
-alexa:
-  smart_home:
-    endpoint: https://api.eu.amazonalexa.com/v3/events
-    filter:
-      include_entities:
-        - light.living_room
-        - switch.kitchen
-    entity_config:
-      light.living_room:
-        name: "Wohnzimmer Licht"
-      switch.kitchen:
-        name: "Küchenschalter"
-template:
-  - sensor:
-      - name: "Total Power3"
-        unique_id: "total_power_sensor3"
-        unit_of_measurement: "W"
-        device_class: power
-        state_class: measurement
-        state: >
-          {{ 
-          states('sensor.shelly_em3_channel_a_power') | float(0) +
-          states('sensor.shelly_em3_channel_b_power') | float(0) +
-          states('sensor.shelly_em3_channel_c_power') | float(0)
-          }}
-      - name: "Prozent Nutzung"
-        unique_id: "pv_prozent_nutzung"
-        unit_of_measurement: "%"
-        state: >
-          {% set total_power = states('sensor.total_power_v2') | float(0) + states('sensor.solaranlage_power') | float(0) %}
-          {% if total_power > 0 %}
-            {{ (100 * states('sensor.solaranlage_power') | float(0) / total_power) | round(1) }}
-          {% else %}
-            0
-          {% endif %}
-      - name: "Total Energy Use1"
-        unique_id: "total_energy_use1"
-        device_class: energy
-        state_class: total_increasing
-        unit_of_measurement: "kWh"
-        state: >
-          {{
-          states('sensor.shelly_em3_channel_a_energy') | float(0) +
-          states('sensor.shelly_em3_channel_b_energy') | float(0) +
-          states('sensor.shelly_em3_channel_c_energy') | float(0)
-          }}
-      - name: "Total Energy Returned1"
-        unique_id: "total_energy_returned1"
-        device_class: energy
-        state_class: total_increasing
-        unit_of_measurement: "kWh"
-        state: >
-          {{
-          states('sensor.shelly_em3_channel_a_energy_returned') | float(0) +
-          states('sensor.shelly_em3_channel_b_energy_returned') | float(0) +
-          states('sensor.shelly_em3_channel_c_energy_returned') | float(0)
-          }}
-      - name: "Aktuelle Solarleistung1"
-        unique_id: "aktuelle_solarleistung1"
-        unit_of_measurement: "W"
-        device_class: power
-        state_class: measurement
-        state: >
-          {{
-          max(0, states('sensor.esphome_web_39b3f0_charging_power_2') | float(0) -
-          states('sensor.esphome_web_39b3f0_discharging_power_2') | float(0) +
-          states('sensor.solaranlage_power') | float(0))
-          }}
-      - name: "Täglicher Stromverbrauch"
-        unit_of_measurement: "kWh"
-        state: >
-          {% set aktueller_wert = states('sensor.stromzahler_energieverbrauch') | float %}
-          {% set startwert = states('input_number.tagesstart_zaehlerstand') | float %}
-          {{ (aktueller_wert - startwert) | round(2) }}
-      - name: "Fritzbox Download Volumen"
-        unit_of_measurement: "MB"
-        state: >
-          {% set rate_kbps = states('sensor.fritz_box_7530_download_durchsatz') | float %}
-          {% set rate_kBps = rate_kbps / 8 %}  # Kilobits pro Sekunde in Kilobytes umrechnen
-          {{ (rate_kBps * 60) / 1024 }}  # Datenvolumen pro Minute in Megabyte
-      - name: "Fritzbox Upload Volumen"
-        unit_of_measurement: "MB"
-        state: >
-          {% set rate_kbps = states('sensor.fritz_box_7530_upload_durchsatz') | float %}
-          {% set rate_kBps = rate_kbps / 8 %}  # Kilobits pro Sekunde in Kilobytes umrechnen
-          {{ (rate_kBps * 60) / 1024 }}  # Datenvolumen pro Minute in Megabyte
-      - name: "Aktueller Strompreis"
-        state: "{{ states('input_number.strompreis') }}"
-        unit_of_measurement: "€/kWh"
-        device_class: monetary
-      - name: "Stromverbrauch Vortag"
-        unique_id: "stromverbrauch_vortag"
-        unit_of_measurement: "kWh"
-        device_class: energy
-        state: >
-          {% set stats = state_attr('sensor.taeglicher_stromverbrauch', 'last_period') %}
-          {{ stats | float(0) }}
-      - name: "Einspeisung Vortag"
-        unique_id: "einspeisung_vortag"
-        unit_of_measurement: "kWh"
-        device_class: energy
-        state: >
-          {% set stats = state_attr('sensor.taegliche_einspeisung', 'last_period') %}
-          {{ stats | float(0) }}
-      - name: "Generiert Vortag (Template)"
-        unique_id: "generiert_vortag_template"
-        unit_of_measurement: "kWh"
-        device_class: energy
-        state: >
-          {% set stats = state_attr('sensor.komplett_solarlieferung', 'last_period') %}
-          {{ stats | float(0) }}
-      - name: "Nächste Müllabholung"
-        state: >-
-          {% set today = now().date().isoformat() %}
-          {% for date in states.sensor.garbage.attributes.keys() | list | sort %}
-            {% if date >= today %}
-              {{ date }} - {{ states.sensor.garbage.attributes[date] }}
-              {% break %}
-            {% endif %}
-          {% endfor %}
-      - name: "Statistik Solarerzeugung Durchschnitt"
-        state: "{{ now().year }}"
-        attributes:
-          data: >
-            {{ states('sensor.gsheet_data') }}
-      - name: "Solarertrag 2022"
-        state: "OK"
-        attributes:
-          values: >
-            {% set raw_data = state_attr('sensor.statistik_solarerzeugung_durchschnitt_mqtt', 'data')[1][1:] %}
-            {{ raw_data | map('replace', ',', '.') | map('float') | list }}
-      - name: "Solarertrag 2023"
-        state: "OK"
-        attributes:
-          values: >
-            {% set raw_data = state_attr('sensor.statistik_solarerzeugung_durchschnitt_mqtt', 'data')[2][1:] %}
-            {{ raw_data | map('replace', ',', '.') | map('float') | list }}
-      - name: "Solarertrag 2024"
-        state: "OK"
-        attributes:
-          values: >
-            {% set raw_data = state_attr('sensor.statistik_solarerzeugung_durchschnitt_mqtt', 'data')[3][1:] %}
-            {{ raw_data | map('replace', ',', '.') | map('float') | list }}
-      - name: "Solarertrag 2025"
-        state: "OK"
-        attributes:
-          values: >
-            {% set raw_data = state_attr('sensor.statistik_solarerzeugung_durchschnitt_mqtt', 'data')[4][1:] %}
-            {{ raw_data | map('replace', ',', '.') | map('float') | list }}
-      - name: "Solarertrag 2022 Werte"
-        state: "{{ state_attr('sensor.solarertrag_2022', 'values')[-1] | float(0) }}"
-        unit_of_measurement: "kWh" # Passen Sie die Einheit an
-        state_class: measurement
-        attributes:
-          alle_werte: "{{ state_attr('sensor.solarertrag_2022', 'values') }}"
-
-      - name: "Kühlschrank Letzte Aktivzeit"
-        unique_id: kuehlschrank_letzte_aktivzeit
-        unit_of_measurement: "min"
-        state: >
-          {% set aktiv_start = states.binary_sensor.kuehlschrank_laeuft.last_changed %}
-          {% if is_state('binary_sensor.kuehlschrank_laeuft', 'on') %}
-            {{ ((now() - aktiv_start).total_seconds() / 60) | round(1) }}
-          {% else %}
-            0
-          {% endif %}
-
-      - name: "Kühlschrank Letzte Pausezeit"
-        unique_id: kuehlschrank_letzte_pausezeit
-        unit_of_measurement: "min"
-        state: >
-          {% set pause_start = states.binary_sensor.kuehlschrank_laeuft.last_changed %}
-          {% if is_state('binary_sensor.kuehlschrank_laeuft', 'off') %}
-            {{ ((now() - pause_start).total_seconds() / 60) | round(1) }}
-          {% else %}
-            0
-          {% endif %}
-sensor:
-  - platform: average
-    name: "Durchschnittsverbrauch"
-    unique_id: "durchschnitt_verbrauch"
-    duration: 60
-    entities:
-      - sensor.total_power_v2
-
-  - platform: average
-    name: "Durchschnittsertrag"
-    unique_id: "durchschnitt_ertrag"
-    duration: 180
-    entities:
-      - sensor.aktuelle_solarleistung1
-
-  - platform: teamtracker
-    league_id: "BUND"
-    team_id: "MUC"
-    name: "Bayern2"
-
-  - platform: integration
-    name: Upload Volume
-    source: sensor.fritz_box_7530_upload_durchsatz
-    unit_prefix: k
-    round: 2
-  - platform: integration
-    name: Download Volume
-    source: sensor.fritz_box_7530_download_durchsatz
-    unit_prefix: k
-    round: 2
-  - platform: statistics
-    name: "Generiert Vortag (Statistik)"
-    entity_id: sensor.solaranlage_energy
-    state_characteristic: change
-    max_age:
-      days: 1
-  - platform: rest
-    name: "Google Sheets Daten"
-    resource: "https://script.google.com/macros/s/AKfycbz4sAiMvufOqL-gv5o7YfjaL4V0eWu9dGren_xg6pV35dE8bMyzaQckKp5WCs6ex5bbdA/exec"
-    scan_interval: 600 # Aktualisiert alle 10 Minuten
-    value_template: "{{ value_json[0] }}" # Falls erforderlich, kann dies angepasst werden
-    json_attributes:
-      - "Gesamtwerte"
-      - "Genergiert"
-      - "Einspeisung"
-      - "Netzverbrauch"
-      - "Solarverbrauch"
-      - "Gesamtverbrauch"
-      - "Durchschnittl. Nutzung"
-      - "Autarkiegrad"
-      - "Ersparnis in € / Tag"
-      - "Ersparnis gesamt"
-      - "Prozent Abgezahlt"
-      - "Gesamnt abgezahlt"
-  - platform: history_stats
-    name: "Kühlschrank Aktivzeit"
-    entity_id: binary_sensor.kuehlschrank_laeuft
-    state: "on"
-    type: time
-    start: "{{ now() - timedelta(hours=24) }}"
-    end: "{{ now() }}"
-
-  - platform: history_stats
-    name: "Kühlschrank Pausezeit"
-    entity_id: binary_sensor.kuehlschrank_laeuft
-    state: "off"
-    type: time
-    start: "{{ now() - timedelta(hours=24) }}"
-    end: "{{ now() }}"
-  - platform: statistics
-    name: "Kühlschrank Durchschnitt Aktivzeit"
-    entity_id: sensor.kuehlschrank_letzte_aktivzeit
-    state_characteristic: mean
-    max_age:
-      hours: 24
-    sampling_size: 10
-
-  - platform: statistics
-    name: "Kühlschrank Durchschnitt Pausezeit"
-    entity_id: sensor.kuehlschrank_letzte_pausezeit
-    state_characteristic: mean
-    max_age:
-      hours: 24
-    sampling_size: 10
-    
-input_datetime:
-  kuehlschrank_ende_aktiv:
-    name: "Ende aktive Phase"
-    has_time: true
-  kuehlschrank_ende_pause:
-    name: "Ende Pause Phase"
-    has_time: true
-
-waste_collection_schedule:
-  sources:
-    - name: awido_de
-      args:
-        customer: Erding
-        city: Oberding
-        street: "Gartenstraße"
-
-binary_sensor:
-  - platform: template
-    sensors:
-      kuehlschrank_laeuft:
-        friendly_name: "Kühlschrank läuft"
-        value_template: "{{ states('sensor.kuehlschrank_power')|float > 50 }}"
-
-mqtt:
-  sensor:
-    - name: "Balkonkraftwerk Leistung AC"
-      state_topic: "inverter/hm600/ch0/P_AC"
-      device_class: power
-      unit_of_measurement: W
-      state_class: measurement
-      unique_id: "BalkonkraftwerkLeistungAC"
-    - name: "Balkonkraftwerk Module 1 Leistung"
-      state_topic: "inverter/hm600/ch1/P_DC"
-      device_class: power
-      unit_of_measurement: W
-      state_class: measurement
-      unique_id: "BalkonkraftwerkModule13Leistung"
-    - name: "Balkonkraftwerk Module 2 Leistung"
-      state_topic: "inverter/hm600/ch2/P_DC"
-      device_class: power
-      unit_of_measurement: W
-      state_class: measurement
-      unique_id: "BalkonkraftwerkModule24Leistung"
-    - name: "Balkonkraftwerk Temperatur"
-      state_topic: "inverter/hm600/ch0/Temp"
-      device_class: temperature
-      unit_of_measurement: °C
-      state_class: measurement
-      unique_id: "BalkonkraftwerkTemperatur"
-    - name: "Balkonkraftwerk Arbeit Tag"
-      state_topic: "inverter/hm600/ch0/YieldDay"
-      device_class: energy
-      unit_of_measurement: Wh
-      state_class: total_increasing
-      unique_id: "BalkonkraftwerkArbeitTag"
-    - name: "Balkonkraftwerk Arbeit Gesamt"
-      state_topic: "inverter/hm600/ch0/YieldTotal"
-      device_class: energy
-      unit_of_measurement: kWh
-      state_class: total_increasing
-      unique_id: "BalkonkraftwerkArbeitGesamt"
-    - name: "version"
-      state_topic: "inverter/version"
-      unique_id: "version_dtu"
-    - name: "Limit"
-      state_topic: "inverter/hm600/ch0/active_PowerLimit"
-      unique_id: "set_powerlimit"
-    - name: "Energy Akkuentladung current"
-      device_class: power
-      unit_of_measurement: "W"
-      state_topic: "esphome-web-39b3f0/sensor/esphome-web-39b3f0_discharging_power"
-      unique_id: "energy_akkuentladung"
-    - name: "Energy Akkuentladung total"
-      device_class: energy
-      unit_of_measurement: "kWh"
-      state_topic: "esphome-web-39b3f0/sensor/esphome-web-39b3f0_discharging_power"
-    - name: "Effizienz HM600"
-      unit_of_measurement: "%"
-      state_topic: "inverter/hm600/ch0/Efficiency"
-      unique_id: "effizienz_hm600"
-    - name: "HM600 Spannung"
-      unit_of_measurement: "V"
-      state_topic: "inverter/hm600/ch1/U_DC"
-    - name: "Waschmaschine Leistung"
-      state_topic: "shellyplus1pm-84cca8771670/status/switch:0"
-      value_template: "{{ value_json.apower }}"
-      unit_of_measurement: "W"
-      device_class: power
-    - name: "Waschmaschine Energieverbrauch"
-      state_topic: "shellyplus1pm-84cca8771670/status/switch:0"
-      value_template: "{{ value_json.aenergy.total }}"
-      unit_of_measurement: "kWh"
-      device_class: energy
-    - name: "Statistik Solarerzeugung Durchschnitt mqtt"
-      state_topic: "homeassistant/sensor/gsheet_data"
-      value_template: "{{ value_json.state }}"
-      json_attributes_topic: "homeassistant/sensor/gsheet_data"
-      json_attributes_template: "{{ value_json.attributes | tojson }}"
-
-logger:
-  default: warning
-  logs:
-    custom_components.awtrix: warning
-    homeassistant.components.sensor: warning
-
-# Nächste Abholung Restmüll
-
-#      - name: "Restmüll"
-#        state: '{{value.types|join(", ")}}{% if value.daysTo == 0 %} Heute{% elif value.daysTo == 1 %} Morgen{% else %} in {{value.daysTo}} Tagen{% endif %}'
-#        attributes:
-#          value_template: '{{value.types|join(", ")}}'
-#        unique_id: "restmuell"
-#        unit_of_measurement: "days"
-#        device_class: "timestamp"
-#        value_template: '{{(states.sensor.waste_collection_schedule.attributes.next_date)|as_timestamp | timestamp_local}}'
-
-# Nächste Abholung Biotonne
-
-#      - name: "Biotonne"
-#        state: '{{value.types|join(", ")}}{% if value.daysTo == 0 %} Heute{% elif value.daysTo == 1 %} Morgen{% else %} in {{value.daysTo}} Tagen{% endif %}'
-#        attributes:
-#          value_template: '{{value.types|join(", ")}}'
-#        unique_id: "biotonne"
-#        unit_of_measurement: "days"
-#        device_class: "timestamp"
-#        value_template: '{{(states.sensor.waste_collection_schedule.attributes.next_date)|as_timestamp | timestamp_local}}'
-
-##sensor:
-#  - platform: average
-#    name: 'Durchschnittsverbrauch'
-#    unique_id: 'durchschnitt_verbrauch'
-#    duration: 60
-#    entities:
-#      - sensor.total_power
-#  - platform: average
-#    name: 'Durchschnittsertrag'
-#    unique_id: 'durchschnitt_ertrag'
-#    duration: 180
-#    entities:
-#      - sensor.aktuelle_solarleistung
-#  - platform: teamtracker
-#    league_id: "BUND"
-#    team_id: "MUC"
-#    name: "Bayern2"
-#
-#  - platform: template
-#    name: "Total Power"
-#    unique_id: "Total_Energy"
-#    device_class: power
-#    state_class: total
-#    unit_of_measurement: "W"
-#    value_template: >
-#      {{
-#      states('sensor.shelly_em3_channel_a_power')| float(0) +
-#      states('sensor.shelly_em3_channel_b_power')| float(0) +
-#      states('sensor.shelly_em3_channel_c_power')| float(0)
-#      }}
-#
-#  - platform: template
-#    name: "Total Energy Use1"
-#    unique_id: "Total_Energy_Use1"
-#    device_class: energy
-#    state_class: total
-#    unit_of_measurement: "kWh"
-#    value_template: >
-#      {{
-#      states('sensor.shelly_em3_channel_a_energy')| float(0) +
-#      states('sensor.shelly_em3_channel_b_energy')| float(0) +
-#      states('sensor.shelly_em3_channel_c_energy')| float(0)
-#      }}
-#
-#  - name: "Total Energy Returned1"
-#    unique_id: "Total_Energy_Returned1"
-#    device_class: energy
-#    state_class: total
-#    unit_of_measurement: "kWh"
-#    value_template: >
-#      {{
-#      states('sensor.shelly_em3_channel_a_energy_returned')| float(0) +
-#      states('sensor.shelly_em3_channel_b_energy_returned')| float(0) +
-#      states('sensor.shelly_em3_channel_c_energy_returned')| float(0)
-#      }}
-#
-#  - name: "PV Einspeisung"
-#    unique_id: "pv_einspeisung"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: "{{ states('sensor.total_power')|float if states('sensor.total_power') | int < 1 else 0 }}"
-#
-#  - name: "PV Einspeisung negiert"
-#    unique_id: "pv_einspeisung_negiert"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: "{{ states('sensor.pv_einspeisung')|float * -1 }}"
-#
-#  - name: "Wirkungsgrad"
-#    unique_id: "wirkungsgrad_battery"
-#    unit_of_measurement: "%"
-#    device_class: power
-#    value_template: >
-#      {{(100 * states('sensor.solaranlage_power')| float(0) / states('sensor.esphome_web_39b3f0_discharging_power')| float(0)) | round(1) }}
-#
-#  - name: "Prozent_Nutzung"
-#    unique_id: "pv_prozent_nutzung"
-#    unit_of_measurement: "%"
-#    device_class: power
-#    value_template: >
-#      {{
-#      (100 * states('sensor.solaranlage_power')| float(0) / (states('sensor.solaranlage_power')| float(0) + states('sensor.total_power_v2')| float(0))) | round(1)
-#      }}
-#
-#  - name: "Aktuelle_Solarleistung"
-#    unique_id: "aktuelle-solarleistung"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: >
-#      {{
-#      max(0, states('sensor.esphome_web_39b3f0_charging_power_2')| float(0) -
-#      states('sensor.esphome_web_39b3f0_discharging_power_2')| float(0) +
-#      states('sensor.solaranlage_power')|float(0) +
-#      }}
-#
-# //states('sensor.akku_power')|float(0)) removed from aktuelle solarleistung
-#
-#  - name: "Summierter Ertrag"
-#    unique_id: "summierter_ertrag"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: >
-#      {{
-#      states('sensor.akku_power')| float(0) +
-#      states('sensor.solaranlage_power')|float(0)
-#      }}
-#
-#  - name: "Total Power"
-#    unique_id: "Total_Energy"
-#    device_class: power
-#    state_class: total
-#    unit_of_measurement: "W"
-#    value_template: >
-#      {{
-#      states('sensor.shelly_em3_channel_a_power')| float(0) +
-#      states('sensor.shelly_em3_channel_b_power')| float(0) +
-#      states('sensor.shelly_em3_channel_c_power')| float(0)
-#      }}
-#
-#  - name: "Total Energy Use"
-#    unique_id: "Total_Energy_Use"
-#    device_class: energy
-#    state_class: total
-#    unit_of_measurement: "kWh"
-#    value_template: >
-#      {{
-#      states('sensor.shelly_em3_channel_a_energy')| float(0) +
-#      states('sensor.shelly_em3_channel_b_energy')| float(0) +
-#      states('sensor.shelly_em3_channel_c_energy')| float(0)
-#      }}
-#
-#  - name: "Total Energy Returned"
-#    unique_id: "Total_Energy_Returned"
-#    device_class: energy
-#    state_class: total
-#    unit_of_measurement: "kWh"
-#    value_template: >
-#      {{
-#      states('sensor.shelly_em3_channel_a_energy_returned')| float(0) +
-#      states('sensor.shelly_em3_channel_b_energy_returned')| float(0) +
-#      states('sensor.shelly_em3_channel_c_energy_returned')| float(0)
-#      }}
-#
-#  - name: "PV Einspeisung"
-#    unique_id: "pv_einspeisung"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: "{{ states('sensor.total_power')|float if states('sensor.total_power') | int < 1 else 0 }}"
-#
-#  - name: "PV Einspeisung negiert"
-#    unique_id: "pv_einspeisung_negiert"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: "{{ states('sensor.pv_einspeisung')|float * -1 }}"
-#
-#  - name: "Wirkungsgrad"
-#    unique_id: "wirkungsgrad_battery"
-#    unit_of_measurement: "%"
-#    device_class: power
-#    value_template: >
-#      {{(100 * states('sensor.solaranlage_power')| float(0) / states('sensor.esphome_web_39b3f0_discharging_power')| float(0)) | round(1) }}###
-#
-#  - name: "Prozent_Nutzung"
-#    unique_id: "pv_prozent_nutzung"
-#    unit_of_measurement: "%"
-#    device_class: power
-#    value_template: >
-#      {{
-#      (100 * states('sensor.solaranlage_power')| float(0) / (states('sensor.solaranlage_power')| float(0) + states('sensor.total_power')| float(0))) | round(1)
-#      }}
-#
-#  - name: "Aktuelle_Solarleistung"
-#    unique_id: "aktuelle-solarleistung"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: >
-#      {{
-#      max(0, states('sensor.esphome_web_39b3f0_charging_power_2')| float(0) -
-#      states('sensor.esphome_web_39b3f0_discharging_power_2')| float(0) +
-#      states('sensor.solaranlage_power')|float(0) +
-#      states('sensor.akku_power')|float(0))
-#      }}
-#
-#  - name: "Summierter Ertrag"
-#    unique_id: "summierter_ertrag"
-#    unit_of_measurement: "W"
-#    device_class: power
-#    value_template: >
-#      {{
-#      states('sensor.akku_power')| float(0) +
-#      states('sensor.solaranlage_power')|float(0)
-#      }}
-
-# https://community.home-assistant.io/t/hoymiles-dtu-microinverters-pv/253674/21
-
-# statistics
-#- platform: statistics
-#  entity_id: sensor.total_power_av
-#  sampling_size: 20
-
-#powercalc:
-
-#sensor:
-#   - platform: powercalc
-#     entity_id: light.esslicht
-#     fixed:
-#      states_power:
-#       off: 0.4
-#       on: 22
-
-# platform: template
-#   daily_solar_percent:
-#    value_template: "{{ ( 100 * states('sensor.total_power')|float / states('sensor.solaranlage_power')|float )|round(1) }}"
-#    unit_of_measurement: '%'
-#    friendly_name: Daily Solar Percentage
-
-# ssl Configuration
-# http:
-#  ssl_certificate: /ssl/fullchain.pem
-#  ssl_key: /ssl/privkey.pem
-
-http:
-  use_x_forwarded_for: true
-  trusted_proxies:
-    - 127.0.0.1
-    - 192.168.178.6
-    - 172.16.0.0/12
-    - ::1
-  #ssl_certificate: "/ssl/fullchain.pem"
-  #ssl_key: "/ssl/privkey.pem"
-homeassistant:
-  external_url: "https://floke-ha.duckdns.org"

HA_jbd_bms.yaml

@@ -1,260 +0,0 @@
-substitutions:
-  name: esphome-web-39b3f0
-  device_description: "Monitor and control a Xiaoxiang Battery Management System (JBD-BMS) via BLE"
-  external_components_source: github://syssi/esphome-jbd-bms@main
-  mac_address: A4:C1:37:00:86:5A
-
-esphome:
-  name: ${name}
-  comment: ${device_description}
-  min_version: 2024.6.0
-  project:
-    name: "syssi.esphome-jbd-bms"
-    version: 2.1.0
-
-esp32:
-  board: esp32dev
-  framework:
-    type: esp-idf
-
-external_components:
-  - source: ${external_components_source}
-    refresh: 0s
-
-wifi:
-  ssid: !secret wifi_ssid
-  password: !secret wifi_password
-
-ota:
-  platform: esphome
-
-logger:
-  level: DEBUG
-
-# If you use Home Assistant please remove this `mqtt` section and uncomment the `api` component!
-# The native API has many advantages over MQTT: https://esphome.io/components/api.html#advantages-over-mqtt
-#mqtt:
-#  broker: !secret mqtt_host
-#  username: !secret mqtt_username
-#  password: !secret mqtt_password
-#  id: mqtt_client
-
-# api:
-
-esp32_ble_tracker:
-  scan_parameters:
-    active: false
-
-ble_client:
-  - id: client0
-    mac_address: ${mac_address}
-
-jbd_bms_ble:
-  - id: bms0
-    ble_client_id: client0
-    # Some Liontron BMS models require an update interval of less than 8s
-    update_interval: 2s
-
-button:
-  - platform: jbd_bms_ble
-    jbd_bms_ble_id: bms0
-    retrieve_hardware_version:
-      name: "${name} retrieve hardware version"
-    force_soc_reset:
-      name: "${name} force soc reset"
-
-binary_sensor:
-  - platform: jbd_bms_ble
-    jbd_bms_ble_id: bms0
-    balancing:
-      name: "${name} balancing"
-    charging:
-      name: "${name} charging"
-    discharging:
-      name: "${name} discharging"
-    online_status:
-      name: "${name} online status"
-
-sensor:
-  - platform: jbd_bms_ble
-    jbd_bms_ble_id: bms0
-    battery_strings:
-      name: "${name} battery strings"
-    current:
-      name: "${name} current"
-    power:
-      name: "${name} power"
-    charging_power:
-      name: "${name} charging power"
-    discharging_power:
-      name: "${name} discharging power"
-    state_of_charge:
-      name: "${name} state of charge"
-    nominal_capacity:
-      name: "${name} nominal capacity"
-    charging_cycles:
-      name: "${name} charging cycles"
-    capacity_remaining:
-      name: "${name} capacity remaining"
-    battery_cycle_capacity:
-      name: "${name} battery cycle capacity"
-    total_voltage:
-      name: "${name} total voltage"
-    average_cell_voltage:
-      name: "${name} average cell voltage"
-    delta_cell_voltage:
-      name: "${name} delta cell voltage"
-    min_cell_voltage:
-      name: "${name} min cell voltage"
-    max_cell_voltage:
-      name: "${name} max cell voltage"
-    min_voltage_cell:
-      name: "${name} min voltage cell"
-    max_voltage_cell:
-      name: "${name} max voltage cell"
-    temperature_1:
-      name: "${name} temperature 1"
-    temperature_2:
-      name: "${name} temperature 2"
-    temperature_3:
-      name: "${name} temperature 3"
-    temperature_4:
-      name: "${name} temperature 4"
-    temperature_5:
-      name: "${name} temperature 5"
-    temperature_6:
-      name: "${name} temperature 6"
-    cell_voltage_1:
-      name: "${name} cell voltage 1"
-    cell_voltage_2:
-      name: "${name} cell voltage 2"
-    cell_voltage_3:
-      name: "${name} cell voltage 3"
-    cell_voltage_4:
-      name: "${name} cell voltage 4"
-    cell_voltage_5:
-      name: "${name} cell voltage 5"
-    cell_voltage_6:
-      name: "${name} cell voltage 6"
-    cell_voltage_7:
-      name: "${name} cell voltage 7"
-    cell_voltage_8:
-      name: "${name} cell voltage 8"
-    cell_voltage_9:
-      name: "${name} cell voltage 9"
-    cell_voltage_10:
-      name: "${name} cell voltage 10"
-    cell_voltage_11:
-      name: "${name} cell voltage 11"
-    cell_voltage_12:
-      name: "${name} cell voltage 12"
-    cell_voltage_13:
-      name: "${name} cell voltage 13"
-    cell_voltage_14:
-      name: "${name} cell voltage 14"
-    cell_voltage_15:
-      name: "${name} cell voltage 15"
-    cell_voltage_16:
-      name: "${name} cell voltage 16"
-    cell_voltage_17:
-      name: "${name} cell voltage 17"
-    cell_voltage_18:
-      name: "${name} cell voltage 18"
-    cell_voltage_19:
-      name: "${name} cell voltage 19"
-    cell_voltage_20:
-      name: "${name} cell voltage 20"
-    cell_voltage_21:
-      name: "${name} cell voltage 21"
-    cell_voltage_22:
-      name: "${name} cell voltage 22"
-    cell_voltage_23:
-      name: "${name} cell voltage 23"
-    cell_voltage_24:
-      name: "${name} cell voltage 24"
-    cell_voltage_25:
-      name: "${name} cell voltage 25"
-    cell_voltage_26:
-      name: "${name} cell voltage 26"
-    cell_voltage_27:
-      name: "${name} cell voltage 27"
-    cell_voltage_28:
-      name: "${name} cell voltage 28"
-    cell_voltage_29:
-      name: "${name} cell voltage 29"
-    cell_voltage_30:
-      name: "${name} cell voltage 30"
-    cell_voltage_31:
-      name: "${name} cell voltage 31"
-    cell_voltage_32:
-      name: "${name} cell voltage 32"
-    operation_status_bitmask:
-      name: "${name} operation status bitmask"
-    errors_bitmask:
-      name: "${name} errors bitmask"
-    balancer_status_bitmask:
-      name: "${name} balancer status bitmask"
-    software_version:
-      name: "${name} software version"
-    short_circuit_error_count:
-      name: "${name} short circuit error count"
-    charge_overcurrent_error_count:
-      name: "${name} charge overcurrent error count"
-    discharge_overcurrent_error_count:
-      name: "${name} discharge overcurrent error count"
-    cell_overvoltage_error_count:
-      name: "${name} cell overvoltage error count"
-    cell_undervoltage_error_count:
-      name: "${name} cell undervoltage error count"
-    charge_overtemperature_error_count:
-      name: "${name} charge overtemperature error count"
-    charge_undertemperature_error_count:
-      name: "${name} charge undertemperature error count"
-    discharge_overtemperature_error_count:
-      name: "${name} discharge overtemperature error count"
-    discharge_undertemperature_error_count:
-      name: "${name} discharge undertemperature error count"
-    battery_overvoltage_error_count:
-      name: "${name} battery overvoltage error count"
-    battery_undervoltage_error_count:
-      name: "${name} battery undervoltage error count"
-
-text_sensor:
-  - platform: jbd_bms_ble
-    jbd_bms_ble_id: bms0
-    errors:
-      name: "${name} errors"
-    operation_status:
-      name: "${name} operation status"
-    device_model:
-      name: "${name} device model"
-
-select:
-  - platform: jbd_bms_ble
-    jbd_bms_ble_id: bms0
-    read_eeprom_register:
-      name: "${name} read eeprom register"
-      id: read_eeprom_register0
-      optionsmap:
-        0xAA: "Error Counts"
-
-switch:
-  - platform: ble_client
-    ble_client_id: client0
-    name: "${name} enable bluetooth connection"
-
-  - platform: jbd_bms_ble
-    jbd_bms_ble_id: bms0
-    charging:
-      name: "${name} charging"
-    discharging:
-      name: "${name} discharging"
-
-# Uncomment this section if you want to update the error count sensors periodically
-#
-# interval:
-#   - interval: 30min
-#     then:
-#       - select.set:
-#           id: read_eeprom_register0
-#           option: "Error Counts"

KONVER_STRATEGY.md

@@ -1,81 +0,0 @@
-# Konver.ai Integration: Strategie & Architektur
-
-**Status:** Vertrag unterzeichnet (Fokus: Telefon-Enrichment).
-**Risiko:** Wegfall von Dealfront (Lead Gen) ohne adäquaten, automatisierten Ersatz.
-**Ziel:** Nutzung von Konver.ai nicht nur als manuelles "Telefonbuch", sondern als **skalierbare Quelle** für die Lead-Fabrik (Company Explorer).
-
-## 1. Das Zielszenario (The "Golden Flow")
-
-Wir integrieren Konver.ai via API direkt in den Company Explorer. Der CE fungiert als Gatekeeper, um Credits zu sparen und Dubletten zu verhindern.
-
-```mermaid
-flowchart TD
-    subgraph "RoboPlanet Ecosystem"
-        Notion[("Notion Strategy\n(Verticals/Pains)")]
-        SO[("SuperOffice CRM\n(Bestand)")]
-        CE["Company Explorer\n(The Brain)"]
-    end
-
-    subgraph "External Sources"
-        Konver["Konver.ai API"]
-        Web["Web / Google / Wiki"]
-    end
-
-    %% Data Flow
-    Notion -->|1. Sync Strategy| CE
-    SO -->|2. Import Existing (Blocklist)| CE
-    
-    CE -->|3. Search Query + Exclusion List| Konver
-    Note right of Konver: "Suche: Altenheime > 10 Mio\nExclude: Domain-Liste aus SO"
-    
-    Konver -->|4. Net New Candidates| CE
-    
-    CE -->|5. Deep Dive (Robotik-Check)| Web
-    
-    CE -->|6. Enrich Contact (Phone/Mail)| Konver
-    Note right of CE: "Nur für Firmen mit\nhohem Robotik-Score!"
-    
-    CE -->|7. Export Qualified Lead| SO
-```
-
-## 2. Die kritische Lücke: "Exclusion List"
-
-Da Dealfront (unser bisheriges "Fischnetz") abgeschaltet wird, müssen wir Konver zur **Neukunden-Generierung** nutzen.
-Ohne eine **Ausschluss-Liste (Exclusion List)** bei der Suche verbrennen wir Geld und Zeit:
-
-1.  **Kosten:** Wir zahlen Credits für Firmen/Kontakte, die wir schon haben.
-2.  **Daten-Hygiene:** Wir importieren Dubletten, die wir mühsam bereinigen müssen.
-3.  **Blindflug:** Wir wissen vor dem Kauf nicht, ob der Datensatz "netto neu" ist.
-
-### Forderung an Konver (Technisches Onboarding)
-
-*"Um Konver.ai als strategischen Nachfolger für Dealfront in unserer Marketing-Automation nutzen zu können, benötigen wir zwingend API-Funktionen zur **Deduplizierung VOR dem Datenkauf**."*
-
-**Konkrete Features:**
-*   **Domain-Exclusion:** Upload einer Liste (z.B. 5.000 Domains), die in der API-Suche *nicht* zurückgegeben werden.
-*   **Contact-Check:** Prüfung (z.B. Hash-Abgleich), ob eine E-Mail-Adresse bereits "bekannt" ist, bevor Kontaktdaten enthüllt (und berechnet) werden.
-
-## 3. Workflow-Varianten
-
-### A. Der "Smart Enricher" (Wirtschaftlich)
-Wir nutzen Konver nur für Firmen, die **wirklich** relevant sind.
-
-1.  **Scraping:** Company Explorer findet 100 Altenheime (Web-Suche).
-2.  **Filterung:** KI prüft Websites -> 40 davon sind relevant (haben große Flächen).
-3.  **Enrichment:** Nur für diese 40 fragen wir Konver via API: *"Gib mir den Facility Manager + Handy"*.
-4.  **Ergebnis:** Wir zahlen 40 Credits statt 100. Hohe Effizienz.
-
-### B. Der "Mass Loader" (Teuer & Dumm - zu vermeiden)
-1.  Wir laden "Alle Altenheime" aus Konver direkt nach SuperOffice.
-2.  Wir zahlen 100 Credits.
-3.  Der Vertrieb ruft an -> 60 davon sind ungeeignet (zu klein, kein Bedarf).
-4.  **Ergebnis:** 60 Credits verbrannt, Vertrieb frustriert.
-
-## 4. Fazit & Next Steps
-
-Wir müssen im Onboarding-Gespräch klären:
-1.  **API-Doku:** Wo ist die Dokumentation für `Search` und `Enrich` Endpoints?
-2.  **Exclusion:** Wie filtern wir Bestandskunden im API-Call?
-3.  **Bulk-Enrichment:** Können wir Listen (Domains) zum Anreichern hochladen?
-
-Ohne diese Features ist Konver ein Rückschritt in die manuelle Einzelbearbeitung.

MIGRATION_PLAN.md

@@ -1,161 +0,0 @@
-# Migrations-Plan: Legacy GSheets -> Company Explorer (Robotics Edition v0.8.5)
-
-**Kontext:** Neuanfang für die Branche **Robotik & Facility Management**.
-**Ziel:** Ablösung von Google Sheets/CLI durch eine Web-App ("Company Explorer") mit SQLite-Backend.
-
-## 1. Strategische Neuausrichtung
-
-| Bereich | Alt (Legacy) | Neu (Robotics Edition) |
-| :--- | :--- | :--- |
-| **Daten-Basis** | Google Sheets | **SQLite** (Lokal, performant, filterbar). |
-| **Ziel-Daten** | Allgemein / Kundenservice | **Quantifizierbares Potenzial** (z.B. 4500m² Fläche, 120 Betten). |
-| **Branchen** | KI-Vorschlag (Freitext) | **Strict Mode:** Mapping auf definierte Notion-Liste (z.B. "Hotellerie", "Automotive"). |
-| **Bewertung** | 0-100 Score (Vage) | **Data-Driven:** Rohwert (Scraper/Search) -> Standardisierung (Formel) -> Potenzial. |
-| **Analytics** | Techniker-ML-Modell | **Deaktiviert**. Fokus auf harte Fakten. |
-| **Operations** | D365 Sync (Broken) | **Excel-Import & Deduplizierung**. Fokus auf Matching externer Listen gegen Bestand. |
-
-## 2. Architektur & Komponenten-Mapping
-
-Das System wird in `company-explorer/` neu aufgebaut. Wir lösen Abhängigkeiten zur Root `helpers.py` auf.
-
-### A. Core Backend (`backend/`)
-
-| Komponente | Aufgabe & Neue Logik | Prio |
-| :--- | :--- | :--- |
-| **Database** | Ersetzt `GoogleSheetHandler`. Speichert Firmen & "Enrichment Blobs". | 1 |
-| **Importer** | Ersetzt `SyncManager`. Importiert Excel-Dumps (CRM) und Event-Listen. | 1 |
-| **Deduplicator** | Ersetzt `company_deduplicator.py`. **Kern-Feature:** Checkt Event-Listen gegen DB. Muss "intelligent" matchen (Name + Ort + Web). | 1 |
-| **Scraper (Base)** | Extrahiert Text von Websites. Basis für alle Analysen. | 1 |
-| **Classification Service** | **NEU (v0.7.0).** Zweistufige Logik: <br> 1. Strict Industry Classification. <br> 2. Metric Extraction Cascade (Web -> Wiki -> SerpAPI). | 1 |
-| **Marketing Engine** | Ersetzt `generate_marketing_text.py`. Nutzt neue `marketing_wissen_robotics.yaml`. | 3 |
-
-**Identifizierte Hauptdatei:** `company-explorer/backend/app.py`
-
-### B. Frontend (`frontend/`) - React
-
-*   **View 1: Der "Explorer":** DataGrid aller Firmen. Filterbar nach "Roboter-Potential" und Status.
-*   **View 2: Der "Inspector":** Detailansicht einer Firma. Zeigt gefundene Signale ("Hat SPA Bereich"). Manuelle Korrektur-Möglichkeit.
-    *   **Identifizierte Komponente:** `company-explorer/frontend/src/components/Inspector.tsx`
-*   **View 3: "List Matcher":** Upload einer Excel-Liste -> Anzeige von Duplikaten -> Button "Neue importieren".
-*   **View 4: "Settings":** Konfiguration von Branchen, Rollen und Robotik-Logik.
-    *   **Frontend "Settings" Komponente:** `company-explorer/frontend/src/components/RoboticsSettings.tsx`
-
-### C. Architekturmuster für die Client-Integration
-
-Um externen Diensten (wie der `lead-engine`) eine einfache und robuste Anbindung an den `company-explorer` zu ermöglichen, wurde ein standardisiertes Client-Connector-Muster implementiert.
-
-| Komponente | Aufgabe & Neue Logik |
-| :--- | :--- |
-| **`company_explorer_connector.py`** | **NEU:** Ein zentrales Python-Skript, das als "offizieller" Client-Wrapper für die API des Company Explorers dient. Es kapselt die Komplexität der asynchronen Enrichment-Prozesse. |
-| **`handle_company_workflow()`** | Die Kernfunktion des Connectors. Sie implementiert den vollständigen "Find-or-Create-and-Enrich"-Workflow: <br> 1. **Prüfen:** Stellt fest, ob ein Unternehmen bereits existiert. <br> 2. **Erstellen:** Legt das Unternehmen an, falls es neu ist. <br> 3. **Anstoßen:** Startet den asynchronen `discover`-Prozess. <br> 4. **Warten (Polling):** Überwacht den Status des Unternehmens, bis eine Website gefunden wurde. <br> 5. **Analysieren:** Startet den asynchronen `analyze`-Prozess. <br> **Vorteil:** Bietet dem aufrufenden Dienst eine einfache, quasi-synchrone Schnittstelle und stellt sicher, dass die Prozessschritte in der korrekten Reihenfolge ausgeführt werden. |
-
-## 3. Umgang mit Shared Code (`helpers.py` & Co.)
-
-Wir kapseln das neue Projekt vollständig ab ("Fork & Clean").
-
-*   **Quelle:** `helpers.py` (Root)
-*   **Ziel:** `company-explorer/backend/lib/core_utils.py`
-*   **Aktion:** Wir kopieren nur relevante Teile und ergänzen sie (z.B. `safe_eval_math`, `run_serp_search`).
-
-## 4. Datenstruktur (SQLite Schema)
-
-### Tabelle `companies` (Stammdaten & Analyse)
-*   `id` (PK)
-*   `name` (String)
-*   `website` (String)
-*   `crm_id` (String, nullable - Link zum D365)
-*   `industry_crm` (String - Die "erlaubte" Branche aus Notion)
-*   `city` (String)
-*   `country` (String - Standard: "DE" oder aus Impressum)
-*   `status` (Enum: NEW, IMPORTED, ENRICHED, QUALIFIED)
-*   **NEU (v0.7.0):**
-    *   `calculated_metric_name` (String - z.B. "Anzahl Betten")
-    *   `calculated_metric_value` (Float - z.B. 180)
-    *   `calculated_metric_unit` (String - z.B. "Betten")
-    *   `standardized_metric_value` (Float - z.B. 4500)
-    *   `standardized_metric_unit` (String - z.B. "m²")
-    *   `metric_source` (String - "website", "wikipedia", "serpapi")
-
-### Tabelle `signals` (Deprecated)
-*   *Veraltet ab v0.7.0. Wird durch quantitative Metriken in `companies` ersetzt.*
-
-### Tabelle `contacts` (Ansprechpartner)
-*   `id` (PK)
-*   `account_id` (FK -> companies.id)
-*   `gender`, `title`, `first_name`, `last_name`, `email`
-*   `job_title` (Visitenkarte)
-*   `role` (Standardisierte Rolle: "Operativer Entscheider", etc.)
-*   `status` (Marketing Status)
-
-### Tabelle `industries` (Branchen-Fokus - Synced from Notion)
-*   `id` (PK)
-*   `notion_id` (String, Unique)
-*   `name` (String - "Vertical" in Notion)
-*   `description` (Text - "Definition" in Notion)
-*   `metric_type` (String - "Metric Type")
-*   `min_requirement` (Float - "Min. Requirement")
-*   `whale_threshold` (Float - "Whale Threshold")
-*   `proxy_factor` (Float - "Proxy Factor")
-*   `scraper_search_term` (String - "Scraper Search Term")
-*   `scraper_keywords` (Text - "Scraper Keywords")
-*   `standardization_logic` (String - "Standardization Logic")
-
-### Tabelle `job_role_mappings` (Rollen-Logik)
-*   `id` (PK)
-*   `pattern` (String - Regex für Jobtitles)
-*   `role` (String - Zielrolle)
-
-## 7. Historie & Fixes (Jan 2026)
-
-    *   **[CRITICAL] v0.7.4: Service Restoration & Logic Fix (Jan 24, 2026)**
-    *   **[STABILITY] v0.7.3: Hardening Metric Parser & Regression Testing (Jan 23, 2026)**
-    *   **[STABILITY] v0.7.2: Robust Metric Parsing (Jan 23, 2026)**
-    *   **[STABILITY] v0.7.1: AI Robustness & UI Fixes (Jan 21, 2026)**
-    *   **[MAJOR] v0.7.0: Quantitative Potential Analysis (Jan 20, 2026)**
-    *   **[UPGRADE] v0.6.x: Notion Integration & UI Improvements**
-
-## 14. Upgrade v2.0 (Feb 18, 2026): "Lead-Fabrik" Erweiterung
-
-Dieses Upgrade transformiert den Company Explorer in das zentrale Gehirn der Lead-Generierung (Vorratskammer).
-
-### 14.1 Detaillierte Logik der neuen Datenfelder
-
-Um Gemini CLI (dem Bautrupp) die Umsetzung zu ermöglichen, hier die semantische Bedeutung der neuen Spalten:
-
-#### Tabelle `companies` (Qualitäts- & Abgleich-Metriken)
-
-*   **`confidence_score` (FLOAT, 0.0 - 1.0):** Indikator für die Sicherheit der KI-Klassifizierung. `> 0.8` = Grün.
-*   **`data_mismatch_score` (FLOAT, 0.0 - 1.0):** Abweichung zwischen CRM-Bestand und Web-Recherche (z.B. Umzug).
-*   **`crm_name`, `crm_address`, `crm_website`, `crm_vat`:** Read-Only Snapshot aus SuperOffice zum Vergleich.
-*   **Status-Flags:** `website_scrape_status` und `wiki_search_status`.
-
-#### Tabelle `industries` (Strategie-Parameter)
-
-*   **`pains` / `gains`:** Strukturierte Textblöcke (getrennt durch `[Primary Product]` und `[Secondary Product]`).
-*   **`ops_focus_secondary` (BOOLEAN):** Steuerung für rollenspezifische Produkt-Priorisierung.
-
----
-
-## 15. Offene Arbeitspakete (Bauleitung)
-
-Anweisungen für den "Bautrupp" (Gemini CLI).
-
-### Task 1: UI-Anpassung - Side-by-Side CRM View & Settings
-(In Arbeit / Teilweise erledigt durch Gemini CLI)
-
-### Task 2: Intelligenter CRM-Importer (Bestandsdaten)
-
-**Ziel:** Importieren der `demo_100.xlsx` in die SQLite-Datenbank.
-
-**Anforderungen:**
-1.  **PLZ-Handling:** Zwingend als **String** einlesen (führende Nullen erhalten).
-2.  **Normalisierung:** Website bereinigen (kein `www.`, `https://`).
-3.  **Matching:** Kaskade über CRM-ID, VAT, Domain, Fuzzy Name.
-4.  **Isolierung:** Nur `crm_` Spalten updaten, Golden Records unberührt lassen.
-
----
-
-## 16. Deployment-Referenz (NAS)
-*   **Pfad:** `/volume1/homes/Floke/python/brancheneinstufung/company-explorer`
-*   **DB:** `/app/companies_v3_fixed_2.db`
-*   **Sync:** `docker exec -it company-explorer python backend/scripts/sync_notion_to_ce_enhanced.py`

b2b-marketing-assistant/Dockerfile

@@ -45,8 +45,6 @@ COPY --from=frontend-builder /app/dist ./dist
 
 # Copy the main Python orchestrator script from the project root
 COPY b2b_marketing_orchestrator.py .
-# Copy Gemini API Key file if it exists in root
-COPY gemini_api_key.txt .
 
 # Expose the port the Node.js server will run on
 EXPOSE 3002

b2b-marketing-assistant/b2b_marketing_orchestrator.py

@@ -333,12 +333,11 @@ PROMPTS = {
 # --- API & SCRAPING HELPERS ---
 
 def load_api_key():
-    try:
+    api_key = os.getenv("GEMINI_API_KEY")
-        with open("gemini_api_key.txt", "r") as f:
+    if not api_key:
-            return f.read().strip()
+        logging.error("GEMINI_API_KEY environment variable not found.")
-    except FileNotFoundError:
-        logging.error("API key file 'gemini_api_key.txt' not found.")
         return None
+    return api_key
 
 def call_gemini_api(prompt, api_key, retries=3):
     url = f"https://generativelanguage.googleapis.com/v1/models/gemini-2.5-flash:generateContent?key={api_key}"