docs: Add core analysis prompts to migration plan

MIGRATION_PLAN.md

@@ -221,3 +221,89 @@ Contacts stehen in 1:n Beziehung zu Accounts. Accounts können einen "Primary Co
     *   Implementierung von Markdown-Cleaning (Stripping von Code-Blocks).
     *   Prompt-Optimierung für tabellarische Markdown-Ausgaben in Phase 5.
     *   Markdown-File Import Feature.
+
+## 8. Eingesetzte Prompts (Account-Analyse)
+
+Dieser Abschnitt dokumentiert die Prompts, die im Backend des **Company Explorers** zur automatisierten Analyse von Unternehmensdaten eingesetzt werden.
+
+### 8.1 Impressum Extraktion (aus `services/scraping.py`)
+
+Dient der Extraktion strukturierter Stammdaten aus dem rohen Text der Impressums-Seite.
+
+**Prompt:**
+
+```python
+prompt = f"""
+Extract the official company details from this German 'Impressum' text.
+Return JSON ONLY. Keys: 'legal_name', 'street', 'zip', 'city', 'country_code', 'email', 'phone', 'ceo_name', 'vat_id'.
+'country_code' should be the two-letter ISO code (e.g., "DE", "CH", "AT").
+If a field is missing, use null.
+
+Text:
+{raw_text}
+"""
+```
+
+**Variablen:**
+*   **`raw_text`**: Der bereinigte HTML-Text der gefundenen Impressums-URL (max. 10.000 Zeichen).
+
+---
+
+### 8.2 Robotics Potential Analyse (aus `services/classification.py`)
+
+Der Kern-Prompt zur Bewertung des Automatisierungspotenzials. Er fasst das Geschäftsmodell zusammen, prüft auf physische Infrastruktur und bewertet spezifische Robotik-Anwendungsfälle.
+
+**Prompt:**
+
+```python
+prompt = f"""
+You are a Senior B2B Market Analyst for 'Roboplanet', a specialized robotics distributor.
+Your task is to analyze the target company based on their website text and create a concise **Dossier**.
+
+--- TARGET COMPANY ---
+Name: {company_name}
+Website Content (Excerpt):
+{website_text[:20000]} 
+
+--- ALLOWED INDUSTRIES (STRICT) ---
+You MUST assign the company to exactly ONE of these industries. If unsure, choose the closest match or "Sonstige".
+{json.dumps(self.allowed_industries, ensure_ascii=False)}
+
+--- ANALYSIS PART 1: BUSINESS MODEL ---
+1. Identify the core products/services.
+2. Summarize in 2-3 German sentences: What do they do and for whom? (Target: "business_model")
+
+--- ANALYSIS PART 2: INFRASTRUCTURE & POTENTIAL (Chain of Thought) ---
+1. **Infrastructure Scan:** Look for evidence of physical assets like *Factories, Large Warehouses, Production Lines, Campuses, Hospitals*.
+2. **Provider vs. User Check:** 
+   - Does the company USE this infrastructure (Potential Customer)?
+   - Or do they SELL products for it (Competitor/Partner)? 
+   - *Example:* "Cleaning" -> Do they sell soap (Provider) or do they have a 50,000sqm factory (User)?
+3. **Evidence Extraction:** Extract 1-2 key sentences from the text proving this infrastructure. (Target: "infrastructure_evidence")
+
+--- ANALYSIS PART 3: SCORING (0-100) ---
+Based on the identified infrastructure, score the potential for these categories:
+
+{category_guidance}
+
+--- OUTPUT FORMAT (JSON ONLY) ---
+{{
+    "industry": "String (from list)",
+    "business_model": "2-3 sentences summary (German)",
+    "infrastructure_evidence": "1-2 key sentences proving physical assets (German)",
+    "potentials": {{
+        "cleaning": {{ "score": 0-100, "reason": "Reasoning based on infrastructure." }},\
+        "transport": {{ "score": 0-100, "reason": "Reasoning based on logistics volume." }},\
+        "security": {{ "score": 0-100, "reason": "Reasoning based on perimeter/assets." }},\
+        "service": {{ "score": 0-100, "reason": "Reasoning based on guest interaction." }}\
+    }}\
+}}
+"""
+```
+
+**Variablen:**
+*   **`company_name`**: Name des Unternehmens.
+*   **`website_text`**: Der gescrapte Text der Hauptseite (max. 20.000 Zeichen).
+*   **`allowed_industries`**: JSON-Liste der erlaubten Branchen (Strict Mode).
+*   **`category_guidance`**: Dynamisch generierte Definitionen und Scoring-Regeln für die Robotik-Kategorien (aus der Datenbank).
+