Protokoll #12378

{
    "event": "PreToolUse",
    "tool_name": "Read",
    "tool_input": {
        "file_path": "\/var\/www\/scripts\/pipeline\/generate.py",
        "offset": 665,
        "limit": 100
    }
}

{
    "tool_response": {
        "type": "text",
        "file": {
            "filePath": "\/var\/www\/scripts\/pipeline\/generate.py",
            "content": "        db.disconnect()\n\n\ndef get_critic(critic_id):\n    \"\"\"Load critic from database.\"\"\"\n    cursor = db.execute(\n        \"\"\"SELECT c.*, p.content as prompt_content\n           FROM critics c\n           LEFT JOIN prompts p ON c.prompt_id = p.id\n           WHERE c.id = %s AND c.is_active = 1\"\"\",\n        (critic_id,),\n    )\n    result = cursor.fetchone()\n    cursor.close()\n    return result\n\n\ndef run_critic(content, critic_id, model=\"anthropic\"):\n    \"\"\"\n    Run a single critic on content.\n\n    Returns:\n        dict with feedback and rating\n    \"\"\"\n    db.connect()\n\n    try:\n        critic = get_critic(critic_id)\n        if not critic:\n            return {\"error\": f\"Critic {critic_id} not found\"}\n\n        fokus = json.loads(critic[\"fokus\"]) if isinstance(critic[\"fokus\"], str) else critic[\"fokus\"]\n        fokus_str = \", \".join(fokus)\n\n        # Load prompt from database (via critic.prompt_id or fallback to generic)\n        prompt_template = critic.get(\"prompt_content\")\n        if not prompt_template:\n            prompt_template = get_prompt(\"critic-generic\")\n        if not prompt_template:\n            # Ultimate fallback - should never happen if DB is properly set up\n            prompt_template = \"\"\"Du bist ein kritischer Lektor mit dem Fokus auf: {fokus}\n\nAnalysiere den folgenden Text und gib strukturiertes Feedback:\n\n## Text:\n{content}\n\n## Deine Aufgabe:\n1. Prüfe den Text auf die Aspekte: {fokus}\n2. Identifiziere konkrete Verbesserungspunkte\n3. Bewerte die Qualität (1-10)\n\nAntworte im JSON-Format:\n{{\n  \"rating\": 8,\n  \"passed\": true,\n  \"issues\": [\"Issue 1\", \"Issue 2\"],\n  \"suggestions\": [\"Suggestion 1\"],\n  \"summary\": \"Kurze Zusammenfassung\"\n}}\"\"\"\n\n        # Format prompt with variables\n        prompt = prompt_template.format(fokus=fokus_str, content=content)\n\n        response = call_llm(prompt, model, client_name=\"content-studio-critique\")\n\n        # Parse JSON from response with robust error handling\n        import re\n\n        json_match = re.search(r\"\\{[\\s\\S]*\\}\", response)\n        if json_match:\n            json_str = json_match.group()\n            try:\n                feedback = json.loads(json_str)\n                feedback[\"critic_name\"] = critic[\"name\"]\n                return feedback\n            except json.JSONDecodeError:\n                # Try to repair common JSON issues\n                repaired = _repair_json(json_str)\n                try:\n                    feedback = json.loads(repaired)\n                    feedback[\"critic_name\"] = critic[\"name\"]\n                    return feedback\n                except json.JSONDecodeError:\n                    pass\n\n        return {\n            \"critic_name\": critic[\"name\"],\n            \"rating\": 5,\n            \"passed\": False,\n            \"issues\": [\"Konnte Feedback nicht parsen\"],\n            \"suggestions\": [],\n            \"summary\": response[:500],\n        }\n\n    except Exception as e:\n        return {\"error\": str(e)}\n    finally:\n        db.disconnect()\n",
            "numLines": 100,
            "startLine": 665,
            "totalLines": 987
        }
    }
}