attempt to improve post creation

src/agents/critic.py

@@ -59,11 +59,25 @@ class CriticAgent(BaseAgent):
 
     def _get_system_prompt(self, profile_analysis: Dict[str, Any], example_posts: Optional[List[str]] = None, iteration: int = 1, max_iterations: int = 3) -> str:
         """Get system prompt for critic - orientiert an bewährten n8n-Prompts."""
-        writing_style = profile_analysis.get("writing_style", {})
-        linguistic = profile_analysis.get("linguistic_fingerprint", {})
-        tone_analysis = profile_analysis.get("tone_analysis", {})
-        phrase_library = profile_analysis.get("phrase_library", {})
+        def _ensure_dict(value: Any) -> Dict[str, Any]:
+            return value if isinstance(value, dict) else {}
+
+        def _extract_from_list(items: Any, key: str) -> Optional[str]:
+            if not isinstance(items, list):
+                return None
+            key_lower = key.lower()
+            for item in items:
+                if isinstance(item, str) and item.lower().startswith(f"{key_lower}:"):
+                    return item.split(":", 1)[1].strip()
+            return None
+
+        writing_style = _ensure_dict(profile_analysis.get("writing_style", {}))
+        linguistic = _ensure_dict(profile_analysis.get("linguistic_fingerprint", {}))
+        tone_analysis = _ensure_dict(profile_analysis.get("tone_analysis", {}))
+        phrase_library = _ensure_dict(profile_analysis.get("phrase_library", {}))
         structure_templates = profile_analysis.get("structure_templates", {})
+        structure_templates_dict = _ensure_dict(structure_templates)
+        audience_insights = _ensure_dict(profile_analysis.get("audience_insights", {}))
 
         # Build example posts section for style comparison
         examples_section = ""
@@ -84,7 +98,10 @@ class CriticAgent(BaseAgent):
         cta_phrases = phrase_library.get('cta_phrases', [])
 
         # Extract structure info
-        primary_structure = structure_templates.get('primary_structure', 'Hook → Body → CTA')
+        if isinstance(structure_templates, list):
+            primary_structure = _extract_from_list(structure_templates, "primary_structure") or "Hook → Body → CTA"
+        else:
+            primary_structure = structure_templates_dict.get('primary_structure', 'Hook → Body → CTA')
 
         # Iteration-aware guidance
         iteration_guidance = ""
@@ -118,7 +135,7 @@ ITERATION {iteration}/{max_iterations} - Fortschritt anerkennen:
 
 REFERENZ-PROFIL (Der Maßstab):
 
-Branche: {profile_analysis.get('audience_insights', {}).get('industry_context', 'Business')}
+Branche: {audience_insights.get('industry_context', 'Business')}
 Perspektive: {writing_style.get('perspective', 'Ich-Perspektive')}
 Ansprache: {writing_style.get('form_of_address', 'Du/Euch')}
 Energie-Level: {linguistic.get('energy_level', 7)}/10 (1=sachlich, 10=explosiv)

src/agents/writer.py

@@ -384,6 +384,10 @@ class WriterAgent(BaseAgent):
         if topic.get('why_this_person'):
             why_section = f"\n**WARUM DU DARÜBER SCHREIBEN SOLLTEST:**\n{topic.get('why_this_person')}\n"
 
+        plan_section = ""
+        if topic.get("post_plan"):
+            plan_section = f"\n**POST-PLAN (VERBINDLICH):**\n{json.dumps(topic.get('post_plan'), ensure_ascii=False, indent=2)}\n"
+
         # User thoughts section
         thoughts_section = ""
         if user_thoughts:
@@ -420,7 +424,7 @@ Schreibe einen authentischen LinkedIn-Post, der:
 {angle_section}{hook_section}
 **KERN-FAKT / INHALT:**
 {topic.get('fact', topic.get('description', ''))}
-{summary_section}{extended_summary_section}{outline_section}{key_points_section}{facts_section}{quotes_section}{source_section}{thoughts_section}
+{summary_section}{extended_summary_section}{outline_section}{key_points_section}{facts_section}{quotes_section}{source_section}{plan_section}{thoughts_section}
 **WARUM RELEVANT:**
 {topic.get('relevance', 'Aktuelles Thema für die Zielgruppe')}
 {why_section}
@@ -587,6 +591,37 @@ Analysiere jeden Entwurf kurz und wähle den besten. Antworte im JSON-Format:
         phrase_library = profile_analysis.get("phrase_library", {})
         structure_templates = profile_analysis.get("structure_templates", {})
 
+        def _ensure_dict(value):
+            return value if isinstance(value, dict) else {}
+
+        def _extract_from_list(items, key):
+            if not isinstance(items, list):
+                return None
+            prefix = f"{key}:"
+            for item in items:
+                text = str(item)
+                if text.lower().startswith(prefix):
+                    return text.split(":", 1)[1].strip()
+            return None
+
+        def _extract_list_from_list(items, key):
+            value = _extract_from_list(items, key)
+            if not value:
+                return []
+            return [part.strip() for part in value.split(",") if part.strip()]
+
+        def _extract_emojis(text: str) -> list:
+            if not text:
+                return []
+            pattern = r"[\U0001F300-\U0001FAFF]"
+            matches = re.findall(pattern, text)
+            return matches or []
+
+        # Normalize optional sections
+        visual_dict = _ensure_dict(visual)
+        content_strategy_dict = _ensure_dict(content_strategy)
+        structure_templates_dict = _ensure_dict(structure_templates)
+
         # Build example posts section (OPTIMIERT: mehr Kontext, weniger kürzen)
         examples_section = ""
         if example_posts and len(example_posts) > 0:
@@ -598,7 +633,13 @@ Analysiere jeden Entwurf kurz und wähle den besten. Antworte im JSON-Format:
             examples_section += "--- Ende Beispiele ---\n"
 
         # Safe extraction of nested values
-        emoji_list = visual.get('emoji_usage', {}).get('emojis', ['🚀'])
+        if isinstance(visual, list):
+            emojis = []
+            for item in visual:
+                emojis.extend(_extract_emojis(str(item)))
+            emoji_list = emojis if emojis else ['🚀']
+        else:
+            emoji_list = visual_dict.get('emoji_usage', {}).get('emojis', ['🚀'])
         emoji_str = ' '.join(emoji_list) if isinstance(emoji_list, list) else str(emoji_list)
         sig_phrases = linguistic.get('signature_phrases', [])
         narrative_anchors = linguistic.get('narrative_anchors', [])
@@ -622,9 +663,22 @@ Analysiere jeden Entwurf kurz und wähle den besten. Antworte im JSON-Format:
             return '\n  - '.join(selected)
 
         # Extract structure templates
-        primary_structure = structure_templates.get('primary_structure', 'Hook → Body → CTA')
-        sentence_starters = structure_templates.get('typical_sentence_starters', [])
-        paragraph_transitions = structure_templates.get('paragraph_transitions', [])
+        if isinstance(structure_templates, list):
+            primary_structure = _extract_from_list(structure_templates, "primary_structure") or "Hook → Body → CTA"
+            sentence_starters = _extract_list_from_list(structure_templates, "typical_sentence_starters")
+            paragraph_transitions = _extract_list_from_list(structure_templates, "paragraph_transitions")
+        else:
+            primary_structure = structure_templates_dict.get('primary_structure', 'Hook → Body → CTA')
+            sentence_starters = structure_templates_dict.get('typical_sentence_starters', [])
+            paragraph_transitions = structure_templates_dict.get('paragraph_transitions', [])
+
+        if isinstance(content_strategy, list):
+            content_strategy_dict = {
+                "cta_style": _extract_from_list(content_strategy, "cta_style") or "Interaktive Frage an die Community",
+                "hook_patterns": _extract_from_list(content_strategy, "hook_patterns"),
+                "post_structure": _extract_from_list(content_strategy, "post_structure"),
+                "storytelling_approach": _extract_from_list(content_strategy, "storytelling_approach"),
+            }
 
         # Extract N-gram patterns if available (NEU!)
         ngram_patterns = profile_analysis.get("ngram_patterns", {})
@@ -799,20 +853,20 @@ Zielgruppe: {audience.get('target_audience', 'Professionals')}
 
 4. VISUELLE REGELN:
 
-Unicode-Fettung: Nutze für den ersten Satz (Hook) fette Unicode-Zeichen (z.B. 𝗪𝗶𝗰𝗵𝘁𝗶𝗴𝗲𝗿 𝗦𝗮𝘁𝘇), sofern das zur Person passt: {visual.get('unicode_formatting', 'Fett für Hooks')}
+Unicode-Fettung: Nutze für den ersten Satz (Hook) fette Unicode-Zeichen (z.B. 𝗪𝗶𝗰𝗵𝘁𝗶𝗴𝗲𝗿 𝗦𝗮𝘁𝘇), sofern das zur Person passt: {visual_dict.get('unicode_formatting', _extract_from_list(visual, 'unicode_formatting') if isinstance(visual, list) else 'Fett für Hooks')}
 
 Emoji-Logik: Verwende diese Emojis: {emoji_str}
-Platzierung: {visual.get('emoji_usage', {}).get('placement', 'Ende')}
-Häufigkeit: {visual.get('emoji_usage', {}).get('frequency', 'Mittel')}
+Platzierung: {visual_dict.get('emoji_usage', {}).get('placement', _extract_from_list(visual, 'emoji_usage') if isinstance(visual, list) else 'Ende')}
+Häufigkeit: {visual_dict.get('emoji_usage', {}).get('frequency', 'Mittel')}
 
 Erzähl-Anker: Baue Elemente ein wie: {narrative_str}
 (Falls 'PS-Zeilen', 'Dialoge' oder 'Flashbacks' genannt sind, integriere diese wenn es passt.)
 
-Layout: {visual.get('structure_preferences', 'Kurze Absätze, mobil-optimiert')}
+Layout: {visual_dict.get('structure_preferences', _extract_from_list(visual, 'structure_preferences') if isinstance(visual, list) else 'Kurze Absätze, mobil-optimiert')}
 
 Länge: Ca. {writing_style.get('average_word_count', 300)} Wörter
 
-CTA: Beende den Post mit einer Variante von: {content_strategy.get('cta_style', 'Interaktive Frage an die Community')}
+CTA: Beende den Post mit einer Variante von: {content_strategy_dict.get('cta_style', 'Interaktive Frage an die Community')}
 
 
 5. GUARDRAILS (VERBOTE!):
@@ -936,6 +990,10 @@ Strategy Weight: {strategy_weight:.1f} / 1.0
             # Revision mode with structured feedback
             score_text = f"**AKTUELLER SCORE:** {critic_result.get('overall_score', 'N/A')}/100\n\n" if critic_result else ""
 
+            plan_section = ""
+            if topic.get("post_plan"):
+                plan_section = f"\n**POST-PLAN (VERBINDLICH):**\n{json.dumps(topic.get('post_plan'), ensure_ascii=False, indent=2)}\n"
+
             return f"""ÜBERARBEITE den Post basierend auf dem Feedback.
 
 **VORHERIGE VERSION:**
@@ -945,6 +1003,7 @@ Strategy Weight: {strategy_weight:.1f} / 1.0
 {feedback}
 {specific_changes_text}
 {improvements_text}
+{plan_section}
 **DEINE AUFGABE:**
 1. Führe die Änderungen durch wie im Feedback beschrieben
 2. Behalte alles bei was gut funktioniert
@@ -999,6 +1058,10 @@ Gib NUR den überarbeiteten Post zurück - keine Kommentare."""
                 source_url = topic.get('source') or ""
                 source_section = f"\n**QUELLE:** {source_title} {source_url}\n"
 
+            plan_section = ""
+            if topic.get("post_plan"):
+                plan_section = f"\n**POST-PLAN (VERBINDLICH):**\n{json.dumps(topic.get('post_plan'), ensure_ascii=False, indent=2)}\n"
+
             # User thoughts section
             thoughts_section = ""
             if user_thoughts:
@@ -1035,7 +1098,7 @@ Schreibe einen authentischen LinkedIn-Post, der:
 {angle_section}{hook_section}
 **KERN-FAKT / INHALT:**
 {topic.get('fact', topic.get('description', ''))}
-{summary_section}{extended_summary_section}{outline_section}{key_points_section}{facts_section}{quotes_section}{source_section}{thoughts_section}
+{summary_section}{extended_summary_section}{outline_section}{key_points_section}{facts_section}{quotes_section}{source_section}{plan_section}{thoughts_section}
 **WARUM RELEVANT:**
 {topic.get('relevance', 'Aktuelles Thema für die Zielgruppe')}
 

src/orchestrator.py

@@ -1,4 +1,5 @@
 """Main orchestrator for the LinkedIn workflow."""
+import json
 from collections import Counter
 from typing import Dict, Any, List, Optional, Callable
 from uuid import UUID
@@ -638,6 +639,19 @@ class WorkflowOrchestrator:
                 company_strategy = company.company_strategy
                 logger.info(f"Loaded company strategy for post creation: {company.name}")
 
+        # Build a structured post plan (used to guide writing)
+        post_plan = await self._build_post_plan(
+            topic=topic,
+            profile_analysis=profile_analysis.full_analysis,
+            company_strategy=company_strategy,
+            strategy_weight=strategy_weight,
+            selected_hook=selected_hook,
+            user_thoughts=user_thoughts,
+            post_type_analysis=post_type_analysis
+        )
+        if post_plan:
+            topic = {**topic, "post_plan": post_plan}
+
         # Writer-Critic loop
         while iteration < max_iterations and not approved:
             iteration += 1
@@ -697,13 +711,45 @@ class WorkflowOrchestrator:
                 max_iterations=max_iterations
             )
 
+            # Style/Strategy scoring (enforce thresholds based on strategy_weight)
+            style_strategy = await self._score_style_strategy(
+                post=current_post,
+                profile_analysis=profile_analysis.full_analysis,
+                company_strategy=company_strategy,
+                strategy_weight=strategy_weight
+            )
+            style_score = style_strategy.get("style_score", 0)
+            strategy_score = style_strategy.get("strategy_score", 0)
+            style_threshold = int(60 + (1 - strategy_weight) * 30)
+            strategy_threshold = int(60 + strategy_weight * 30)
+            if not company_strategy:
+                strategy_score = 100
+                strategy_threshold = 0
+            critic_result["style_score"] = style_score
+            critic_result["strategy_score"] = strategy_score
+            critic_result["style_threshold"] = style_threshold
+            critic_result["strategy_threshold"] = strategy_threshold
+
+            needs_style_fix = style_score < style_threshold
+            needs_strategy_fix = strategy_score < strategy_threshold
+            if needs_style_fix or needs_strategy_fix:
+                critic_result["approved"] = False
+                issues = []
+                if needs_style_fix:
+                    issues.append(f"Stil-Match zu niedrig ({style_score}/100, Ziel: {style_threshold}).")
+                if needs_strategy_fix:
+                    issues.append(f"Strategie-Match zu niedrig ({strategy_score}/100, Ziel: {strategy_threshold}).")
+                feedback_note = " ".join(issues)
+                extra = style_strategy.get("feedback", "")
+                critic_result["feedback"] = f"{critic_result.get('feedback','')}\n\n{feedback_note}\n{extra}".strip()
+
             critic_feedback_list.append(critic_result)
 
             approved = critic_result.get("approved", False)
             score = critic_result.get("overall_score", 0)
 
             # Auto-approve on last iteration if score is decent (>= 80)
-            if iteration == max_iterations and not approved and score >= 80:
+            if iteration == max_iterations and not approved and score >= 80 and not (needs_style_fix or needs_strategy_fix):
                 approved = True
                 critic_result["approved"] = True
                 logger.info(f"Auto-approved on final iteration with score {score}")
@@ -838,6 +884,138 @@ class WorkflowOrchestrator:
             "readability_check": readability_result
         }
 
+    async def _build_post_plan(
+        self,
+        topic: Dict[str, Any],
+        profile_analysis: Dict[str, Any],
+        company_strategy: Optional[Dict[str, Any]],
+        strategy_weight: float,
+        selected_hook: str = "",
+        user_thoughts: str = "",
+        post_type_analysis: Optional[Dict[str, Any]] = None
+    ) -> Optional[Dict[str, Any]]:
+        """Create a structured plan to guide the writer."""
+        try:
+            system_prompt = (
+                "Du erstellst einen kompakten Schreibplan für einen LinkedIn-Post. "
+                "Der Plan muss Stil der Person UND Unternehmensstrategie berücksichtigen. "
+                "Antwort nur als JSON."
+            )
+
+            user_prompt = f"""
+Thema: {topic.get('title')}
+Kern-Fakt: {topic.get('fact') or topic.get('summary','')}
+Relevanz: {topic.get('relevance')}
+
+Ausgewählter Hook: {selected_hook or "keiner"}
+Persönliche Gedanken: {user_thoughts or "keine"}
+
+Profil-Analyse (Kurz):
+Tone: {profile_analysis.get('writing_style', {}).get('tone')}
+Perspektive: {profile_analysis.get('writing_style', {}).get('perspective')}
+Ansprache: {profile_analysis.get('writing_style', {}).get('form_of_address')}
+Signature Phrases: {profile_analysis.get('linguistic_fingerprint', {}).get('signature_phrases', [])}
+
+Unternehmensstrategie (Kurz):
+Mission: {company_strategy.get('mission') if company_strategy else ''}
+Brand Voice: {company_strategy.get('brand_voice') if company_strategy else ''}
+Content Pillars: {company_strategy.get('content_pillars') if company_strategy else []}
+Do: {company_strategy.get('dos') if company_strategy else []}
+Don't: {company_strategy.get('donts') if company_strategy else []}
+
+Strategy Weight: {strategy_weight:.1f}
+
+Gib JSON im Format:
+{{
+  "hook_type": "Story/Fakten/These/Frage/...",
+  "structure": ["Hook", "Body", "CTA"],
+  "style_requirements": ["2-4 konkrete Stil-Elemente/Signaturphrasen"],
+  "strategy_requirements": ["2-4 Strategie-Elemente/Content-Pillar/Do"],
+  "cta_type": "Frage/Aufforderung/Diskussion/..."
+}}
+"""
+            raw = await self.writer.call_openai(
+                system_prompt=system_prompt,
+                user_prompt=user_prompt,
+                model="gpt-4o",
+                temperature=0.2,
+                response_format={"type": "json_object"}
+            )
+            return json.loads(raw)
+        except Exception as e:
+            logger.warning(f"Post plan generation failed: {e}")
+            return None
+
+    async def _score_style_strategy(
+        self,
+        post: str,
+        profile_analysis: Dict[str, Any],
+        company_strategy: Optional[Dict[str, Any]],
+        strategy_weight: float
+    ) -> Dict[str, Any]:
+        """Score style and strategy match separately."""
+        try:
+            system_prompt = (
+                "Bewerte einen LinkedIn-Post in zwei Dimensionen: Stil-Match zur Person "
+                "und Strategie-Match zur Unternehmensstrategie. Antwort nur als JSON."
+            )
+            user_prompt = f"""
+POST:
+\"\"\"{post}\"\"\"
+
+Profil-Analyse (Kurz):
+Tone: {profile_analysis.get('writing_style', {}).get('tone')}
+Perspektive: {profile_analysis.get('writing_style', {}).get('perspective')}
+Ansprache: {profile_analysis.get('writing_style', {}).get('form_of_address')}
+Signature Phrases: {profile_analysis.get('linguistic_fingerprint', {}).get('signature_phrases', [])}
+
+Unternehmensstrategie (Kurz):
+Mission: {company_strategy.get('mission') if company_strategy else ''}
+Brand Voice: {company_strategy.get('brand_voice') if company_strategy else ''}
+Content Pillars: {company_strategy.get('content_pillars') if company_strategy else []}
+Do: {company_strategy.get('dos') if company_strategy else []}
+Don't: {company_strategy.get('donts') if company_strategy else []}
+
+Gib JSON im Format:
+{{
+  "style_score": 0-100,
+  "strategy_score": 0-100,
+  "feedback": "Kurzes, konkretes Verbesserungshinweis-Text (2-4 Sätze)"
+}}
+"""
+            raw = await self.critic.call_openai(
+                system_prompt=system_prompt,
+                user_prompt=user_prompt,
+                model="gpt-4o",
+                temperature=0.2,
+                response_format={"type": "json_object"}
+            )
+            return json.loads(raw)
+        except Exception as e:
+            logger.warning(f"Style/Strategy scoring failed: {e}")
+            return {"style_score": 0, "strategy_score": 0, "feedback": ""}
+
+    async def generate_post_plan(
+        self,
+        topic: Dict[str, Any],
+        profile_analysis: Dict[str, Any],
+        company_strategy: Optional[Dict[str, Any]],
+        strategy_weight: float,
+        selected_hook: str = "",
+        user_thoughts: str = "",
+        post_type_analysis: Optional[Dict[str, Any]] = None
+    ) -> Optional[Dict[str, Any]]:
+        """Public wrapper to build a post plan."""
+        return await self._build_post_plan(
+            topic=topic,
+            profile_analysis=profile_analysis,
+            company_strategy=company_strategy,
+            strategy_weight=strategy_weight,
+            selected_hook=selected_hook,
+            user_thoughts=user_thoughts,
+            post_type_analysis=post_type_analysis
+        )
+
     async def _extract_recurring_feedback(self, user_id: UUID) -> Dict[str, Any]:
         """
         Extract recurring feedback patterns from past generated posts.

src/utils/profile_analysis_normalizer.py

@@ -0,0 +1,71 @@
+"""Normalize profile analysis structures for consistent access."""
+from typing import Any, Dict, List
+
+
+def _extract_from_list(items: Any, key: str) -> str:
+    if not isinstance(items, list):
+        return ""
+    prefix = f"{key}:"
+    for item in items:
+        text = str(item)
+        if text.lower().startswith(prefix):
+            return text.split(":", 1)[1].strip()
+    return ""
+
+
+def _extract_list_from_list(items: Any, key: str) -> List[str]:
+    value = _extract_from_list(items, key)
+    if not value:
+        return []
+    return [part.strip() for part in value.split(",") if part.strip()]
+
+
+def _ensure_dict(value: Any) -> Dict[str, Any]:
+    return value if isinstance(value, dict) else {}
+
+
+def normalize_profile_analysis(profile_analysis: Dict[str, Any]) -> Dict[str, Any]:
+    """Ensure nested fields are dicts with expected keys to avoid crashes."""
+    if not isinstance(profile_analysis, dict):
+        return {}
+
+    normalized = dict(profile_analysis)
+
+    visual = profile_analysis.get("visual_patterns", {})
+    content_strategy = profile_analysis.get("content_strategy", {})
+    structure_templates = profile_analysis.get("structure_templates", {})
+
+    if isinstance(visual, list):
+        normalized["visual_patterns"] = {
+            "unicode_formatting": _extract_from_list(visual, "unicode_formatting"),
+            "structure_preferences": _extract_from_list(visual, "structure_preferences"),
+            "emoji_usage": {
+                "placement": _extract_from_list(visual, "emoji_usage"),
+                "frequency": _extract_from_list(visual, "emoji_usage"),
+                "emojis": []
+            }
+        }
+    else:
+        normalized["visual_patterns"] = _ensure_dict(visual)
+
+    if isinstance(content_strategy, list):
+        normalized["content_strategy"] = {
+            "cta_style": _extract_from_list(content_strategy, "cta_style"),
+            "hook_patterns": _extract_from_list(content_strategy, "hook_patterns"),
+            "post_structure": _extract_from_list(content_strategy, "post_structure"),
+            "storytelling_approach": _extract_from_list(content_strategy, "storytelling_approach"),
+        }
+    else:
+        normalized["content_strategy"] = _ensure_dict(content_strategy)
+
+    if isinstance(structure_templates, list):
+        normalized["structure_templates"] = {
+            "primary_structure": _extract_from_list(structure_templates, "primary_structure"),
+            "template_examples": _extract_list_from_list(structure_templates, "template_examples"),
+            "paragraph_transitions": _extract_list_from_list(structure_templates, "paragraph_transitions"),
+            "typical_sentence_starters": _extract_list_from_list(structure_templates, "typical_sentence_starters"),
+        }
+    else:
+        normalized["structure_templates"] = _ensure_dict(structure_templates)
+
+    return normalized

src/web/user/routes.py

@@ -4373,6 +4373,16 @@ async def chat_generate_post(request: Request):
             "relevance": "User-specified content"
         }
 
+        plan = await orchestrator.generate_post_plan(
+            topic=topic,
+            profile_analysis=profile_analysis.full_analysis,
+            company_strategy=company_strategy,
+            strategy_weight=getattr(post_type, 'strategy_weight', 0.5),
+            user_thoughts=message
+        )
+        if plan:
+            topic["post_plan"] = plan
+
         # Generate post
         post_content = await writer.process(
             topic=topic,
@@ -4485,6 +4495,16 @@ async def chat_refine_post(request: Request):
             "relevance": "User refinement request"
         }
 
+        plan = await orchestrator.generate_post_plan(
+            topic=topic,
+            profile_analysis=full_analysis,
+            company_strategy=company_strategy,
+            strategy_weight=getattr(post_type, 'strategy_weight', 0.5),
+            user_thoughts=message
+        )
+        if plan:
+            topic["post_plan"] = plan
+
         # Use writer's revision capability
         refined_post = await writer.process(
             topic=topic,
@@ -4794,6 +4814,16 @@ async def company_chat_generate_post(request: Request):
         )
 
         topic = {"title": message[:100], "fact": message, "relevance": "Company-created content"}
+
+        plan = await orchestrator.generate_post_plan(
+            topic=topic,
+            profile_analysis=profile_analysis.full_analysis,
+            company_strategy=company_strategy,
+            strategy_weight=getattr(post_type, 'strategy_weight', 0.5),
+            user_thoughts=message
+        )
+        if plan:
+            topic["post_plan"] = plan
         post_content = await writer.process(
             topic=topic,
             profile_analysis=profile_analysis.full_analysis,