{"type":"queue-operation","operation":"dequeue","timestamp":"2026-02-04T19:51:52.427Z","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3"}
{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","type":"user","message":{"role":"user","content":"Review linkedin-generator.py, photo-analyzer.py, blog-writer.py. Check code quality, error handling, security, production-readiness. Give Issues (Critical/Medium/Minor), Recommendations, Quality Score 1-10 per script. Be concise. Then exit."},"uuid":"7991aa3e-42cb-44e5-ba9e-f9df015441be","timestamp":"2026-02-04T19:51:52.471Z","permissionMode":"default"}
{"parentUuid":"7991aa3e-42cb-44e5-ba9e-f9df015441be","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","message":{"model":"claude-opus-4-5-20251101","id":"msg_013UMT3dWHNKSSCUzQn4WzBi","type":"message","role":"assistant","content":[{"type":"text","text":"\n\nI'll review all three scripts in parallel."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1568,"cache_read_input_tokens":18142,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1568},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CXocSnZiptB7iTLcyQH63","type":"assistant","uuid":"f2e928f5-13e1-420e-81ce-568947a2e2ed","timestamp":"2026-02-04T19:51:55.263Z"}
{"parentUuid":"f2e928f5-13e1-420e-81ce-568947a2e2ed","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","message":{"model":"claude-opus-4-5-20251101","id":"msg_013UMT3dWHNKSSCUzQn4WzBi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C53Y17sF3JdaMi8n1Dyzxk","name":"Read","input":{"file_path":"/home/ccuser/rateright-growth/rivet/scripts/linkedin-generator.py"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1568,"cache_read_input_tokens":18142,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1568},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CXocSnZiptB7iTLcyQH63","type":"assistant","uuid":"abc7133d-b748-4bc5-af5f-54a86dd996ae","timestamp":"2026-02-04T19:51:55.912Z"}
{"parentUuid":"abc7133d-b748-4bc5-af5f-54a86dd996ae","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","slug":"wild-snacking-clover","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01C53Y17sF3JdaMi8n1Dyzxk","type":"tool_result","content":"     1→#!/usr/bin/env python3\n     2→\"\"\"\n     3→RateRight LinkedIn Post Generator\n     4→\n     5→Generate professional LinkedIn posts for the construction industry with multiple length variations.\n     6→\n     7→Usage:\n     8→    python linkedin-generator.py \"topic/theme\"\n     9→    \n    10→Example:\n    11→    python linkedin-generator.py \"steel fixing crew shortage in Sydney\"\n    12→    python linkedin-generator.py \"why formwork quality matters for project success\"\n    13→\n    14→Output:\n    15→    - Short post (50-100 words)\n    16→    - Medium post (150-200 words) \n    17→    - Long post (250-300 words)\n    18→    - Each includes hashtags and call-to-action\n    19→\"\"\"\n    20→\n    21→import sys\n    22→import json\n    23→import logging\n    24→from datetime import datetime\n    25→from pathlib import Path\n    26→\n    27→try:\n    28→    import openai\n    29→except ImportError:\n    30→    print(\"OpenAI library not found. Installing...\")\n    31→    import subprocess\n    32→    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"openai\"])\n    33→    import openai\n    34→\n    35→# Setup logging\n    36→log_dir = Path(\"/home/ccuser/rateright-growth/rivet/memory\")\n    37→log_dir.mkdir(exist_ok=True)\n    38→logging.basicConfig(\n    39→    level=logging.INFO,\n    40→    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    41→    handlers=[\n    42→        logging.FileHandler(log_dir / \"content-engine.log\"),\n    43→        logging.StreamHandler()\n    44→    ]\n    45→)\n    46→logger = logging.getLogger(__name__)\n    47→\n    48→def load_openai_key():\n    49→    \"\"\"Load OpenAI API key from secrets file.\"\"\"\n    50→    try:\n    51→        secrets_path = \"/root/.clawdbot/secrets.json\"\n    52→        with open(secrets_path, 'r') as f:\n    53→            secrets = json.load(f)\n    54→            return secrets.get(\"openai_api_key\") or secrets.get(\"OPENAI_API_KEY\")\n    55→    except Exception as e:\n    56→        logger.error(f\"Failed to load OpenAI key: {e}\")\n    57→        return None\n    58→\n    59→def generate_linkedin_posts(topic):\n    60→    \"\"\"Generate LinkedIn posts using OpenAI API.\"\"\"\n    61→    \n    62→    api_key = load_openai_key()\n    63→    if not api_key:\n    64→        logger.error(\"OpenAI API key not found\")\n    65→        return None\n    66→    \n    67→    client = openai.OpenAI(api_key=api_key)\n    68→    \n    69→    system_prompt = \"\"\"You are a content marketing expert for RateRight, a construction hiring marketplace in Australia. \n    70→    Create professional LinkedIn posts that speak to contractors and construction professionals.\n    71→    \n    72→    Tone: Professional but relatable, contractor-to-contractor, data-driven\n    73→    Focus: Construction industry insights, hiring challenges, workforce trends\n    74→    Audience: Sydney-based contractors, project managers, construction business owners\n    75→    \n    76→    Include:\n    77→    - Industry-specific insights and data\n    78→    - Practical takeaways for contractors\n    79→    - Call-to-action related to hiring or workforce management\n    80→    - Relevant hashtags (max 8 per post)\n    81→    - Australian spelling and terminology\"\"\"\n    82→\n    83→    user_prompt = f\"\"\"Create 3 LinkedIn posts about: {topic}\n    84→\n    85→Requirements:\n    86→1. Short version (50-100 words) - Quick insight with 1-2 key points\n    87→2. Medium version (150-200 words) - Deeper analysis with 2-3 key points  \n    88→3. Long version (250-300 words) - Comprehensive insight with data/examples\n    89→\n    90→Each post must:\n    91→- Start with a strong hook\n    92→- Include construction industry context\n    93→- Provide actionable insights\n    94→- End with a clear call-to-action\n    95→- Use 6-8 relevant hashtags\n    96→- Reference Sydney/Australia construction market when relevant\n    97→\n    98→Topic: {topic}\"\"\"\n    99→\n   100→    try:\n   101→        response = client.chat.completions.create(\n   102→            model=\"gpt-4\",\n   103→            messages=[\n   104→                {\"role\": \"system\", \"content\": system_prompt},\n   105→                {\"role\": \"user\", \"content\": user_prompt}\n   106→            ],\n   107→            temperature=0.7,\n   108→            max_tokens=1500\n   109→        )\n   110→        \n   111→        content = response.choices[0].message.content\n   112→        logger.info(f\"Successfully generated LinkedIn posts for topic: {topic}\")\n   113→        return content\n   114→        \n   115→    except Exception as e:\n   116→        logger.error(f\"OpenAI API error: {e}\")\n   117→        return None\n   118→\n   119→def format_output(content, topic):\n   120→    \"\"\"Format the generated content for display.\"\"\"\n   121→    timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n   122→    \n   123→    output = f\"\"\"# RateRight LinkedIn Post Generator\n   124→Generated on: {timestamp}\n   125→Topic: {topic}\n   126→\n   127→{'='*60}\n   128→{content}\n   129→{'='*60}\n   130→\n   131→\"\"\"\n   132→    return output\n   133→\n   134→def main():\n   135→    \"\"\"Main function to handle command line execution.\"\"\"\n   136→    \n   137→    if len(sys.argv) < 2:\n   138→        print(\"Usage: python linkedin-generator.py 'topic/theme'\")\n   139→        print(\"Example: python linkedin-generator.py 'steel fixing crew shortage in Sydney'\")\n   140→        sys.exit(1)\n   141→    \n   142→    topic = \" \".join(sys.argv[1:])\n   143→    logger.info(f\"Starting LinkedIn post generation for topic: {topic}\")\n   144→    \n   145→    # Generate posts\n   146→    content = generate_linkedin_posts(topic)\n   147→    \n   148→    if content:\n   149→        formatted_output = format_output(content, topic)\n   150→        print(formatted_output)\n   151→        logger.info(\"LinkedIn posts generated successfully\")\n   152→    else:\n   153→        print(\"Failed to generate LinkedIn posts. Check logs for details.\")\n   154→        logger.error(\"Failed to generate LinkedIn posts\")\n   155→        sys.exit(1)\n   156→\n   157→if __name__ == \"__main__\":\n   158→    main()\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"b11df64e-3166-47b8-9059-21a7159185eb","timestamp":"2026-02-04T19:51:55.922Z","toolUseResult":{"type":"text","file":{"filePath":"/home/ccuser/rateright-growth/rivet/scripts/linkedin-generator.py","content":"#!/usr/bin/env python3\n\"\"\"\nRateRight LinkedIn Post Generator\n\nGenerate professional LinkedIn posts for the construction industry with multiple length variations.\n\nUsage:\n    python linkedin-generator.py \"topic/theme\"\n    \nExample:\n    python linkedin-generator.py \"steel fixing crew shortage in Sydney\"\n    python linkedin-generator.py \"why formwork quality matters for project success\"\n\nOutput:\n    - Short post (50-100 words)\n    - Medium post (150-200 words) \n    - Long post (250-300 words)\n    - Each includes hashtags and call-to-action\n\"\"\"\n\nimport sys\nimport json\nimport logging\nfrom datetime import datetime\nfrom pathlib import Path\n\ntry:\n    import openai\nexcept ImportError:\n    print(\"OpenAI library not found. Installing...\")\n    import subprocess\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"openai\"])\n    import openai\n\n# Setup logging\nlog_dir = Path(\"/home/ccuser/rateright-growth/rivet/memory\")\nlog_dir.mkdir(exist_ok=True)\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    handlers=[\n        logging.FileHandler(log_dir / \"content-engine.log\"),\n        logging.StreamHandler()\n    ]\n)\nlogger = logging.getLogger(__name__)\n\ndef load_openai_key():\n    \"\"\"Load OpenAI API key from secrets file.\"\"\"\n    try:\n        secrets_path = \"/root/.clawdbot/secrets.json\"\n        with open(secrets_path, 'r') as f:\n            secrets = json.load(f)\n            return secrets.get(\"openai_api_key\") or secrets.get(\"OPENAI_API_KEY\")\n    except Exception as e:\n        logger.error(f\"Failed to load OpenAI key: {e}\")\n        return None\n\ndef generate_linkedin_posts(topic):\n    \"\"\"Generate LinkedIn posts using OpenAI API.\"\"\"\n    \n    api_key = load_openai_key()\n    if not api_key:\n        logger.error(\"OpenAI API key not found\")\n        return None\n    \n    client = openai.OpenAI(api_key=api_key)\n    \n    system_prompt = \"\"\"You are a content marketing expert for RateRight, a construction hiring marketplace in Australia. \n    Create professional LinkedIn posts that speak to contractors and construction professionals.\n    \n    Tone: Professional but relatable, contractor-to-contractor, data-driven\n    Focus: Construction industry insights, hiring challenges, workforce trends\n    Audience: Sydney-based contractors, project managers, construction business owners\n    \n    Include:\n    - Industry-specific insights and data\n    - Practical takeaways for contractors\n    - Call-to-action related to hiring or workforce management\n    - Relevant hashtags (max 8 per post)\n    - Australian spelling and terminology\"\"\"\n\n    user_prompt = f\"\"\"Create 3 LinkedIn posts about: {topic}\n\nRequirements:\n1. Short version (50-100 words) - Quick insight with 1-2 key points\n2. Medium version (150-200 words) - Deeper analysis with 2-3 key points  \n3. Long version (250-300 words) - Comprehensive insight with data/examples\n\nEach post must:\n- Start with a strong hook\n- Include construction industry context\n- Provide actionable insights\n- End with a clear call-to-action\n- Use 6-8 relevant hashtags\n- Reference Sydney/Australia construction market when relevant\n\nTopic: {topic}\"\"\"\n\n    try:\n        response = client.chat.completions.create(\n            model=\"gpt-4\",\n            messages=[\n                {\"role\": \"system\", \"content\": system_prompt},\n                {\"role\": \"user\", \"content\": user_prompt}\n            ],\n            temperature=0.7,\n            max_tokens=1500\n        )\n        \n        content = response.choices[0].message.content\n        logger.info(f\"Successfully generated LinkedIn posts for topic: {topic}\")\n        return content\n        \n    except Exception as e:\n        logger.error(f\"OpenAI API error: {e}\")\n        return None\n\ndef format_output(content, topic):\n    \"\"\"Format the generated content for display.\"\"\"\n    timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n    \n    output = f\"\"\"# RateRight LinkedIn Post Generator\nGenerated on: {timestamp}\nTopic: {topic}\n\n{'='*60}\n{content}\n{'='*60}\n\n\"\"\"\n    return output\n\ndef main():\n    \"\"\"Main function to handle command line execution.\"\"\"\n    \n    if len(sys.argv) < 2:\n        print(\"Usage: python linkedin-generator.py 'topic/theme'\")\n        print(\"Example: python linkedin-generator.py 'steel fixing crew shortage in Sydney'\")\n        sys.exit(1)\n    \n    topic = \" \".join(sys.argv[1:])\n    logger.info(f\"Starting LinkedIn post generation for topic: {topic}\")\n    \n    # Generate posts\n    content = generate_linkedin_posts(topic)\n    \n    if content:\n        formatted_output = format_output(content, topic)\n        print(formatted_output)\n        logger.info(\"LinkedIn posts generated successfully\")\n    else:\n        print(\"Failed to generate LinkedIn posts. Check logs for details.\")\n        logger.error(\"Failed to generate LinkedIn posts\")\n        sys.exit(1)\n\nif __name__ == \"__main__\":\n    main()","numLines":158,"startLine":1,"totalLines":158}},"sourceToolAssistantUUID":"abc7133d-b748-4bc5-af5f-54a86dd996ae"}
{"parentUuid":"b11df64e-3166-47b8-9059-21a7159185eb","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","slug":"wild-snacking-clover","message":{"model":"claude-opus-4-5-20251101","id":"msg_013UMT3dWHNKSSCUzQn4WzBi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GACVLykBRWUYqhZJNciZW5","name":"Read","input":{"file_path":"/home/ccuser/rateright-growth/rivet/scripts/photo-analyzer.py"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1568,"cache_read_input_tokens":18142,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1568},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CXocSnZiptB7iTLcyQH63","type":"assistant","uuid":"0bc18b47-cf47-41ad-8600-b585edcdcafc","timestamp":"2026-02-04T19:51:56.151Z"}
{"parentUuid":"0bc18b47-cf47-41ad-8600-b585edcdcafc","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","slug":"wild-snacking-clover","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GACVLykBRWUYqhZJNciZW5","type":"tool_result","content":"     1→#!/usr/bin/env python3\n     2→\"\"\"\n     3→RateRight Construction Photo Analyzer\n     4→\n     5→Analyze construction site photos to identify trade types, safety gear, equipment, and conditions.\n     6→Generates marketing insights and post caption suggestions.\n     7→\n     8→Usage:\n     9→    python photo-analyzer.py /path/to/photo.jpg\n    10→    \n    11→Example:\n    12→    python photo-analyzer.py ./site-photos/formwork-crew.jpg\n    13→    python photo-analyzer.py https://example.com/construction-photo.jpg\n    14→\n    15→Output:\n    16→    - Trade type identification\n    17→    - Safety equipment assessment\n    18→    - Equipment and tools identified\n    19→    - Site conditions evaluation\n    20→    - Marketing insights (e.g., \"steel fixing crew needed\")\n    21→    - Social media caption suggestions\n    22→\"\"\"\n    23→\n    24→import sys\n    25→import json\n    26→import logging\n    27→from pathlib import Path\n    28→import base64\n    29→import requests\n    30→\n    31→try:\n    32→    import openai\n    33→except ImportError:\n    34→    print(\"OpenAI library not found. Installing...\")\n    35→    import subprocess\n    36→    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"openai\"])\n    37→    import openai\n    38→\n    39→# Setup logging\n    40→log_dir = Path(\"/home/ccuser/rateright-growth/rivet/memory\")\n    41→log_dir.mkdir(exist_ok=True)\n    42→logging.basicConfig(\n    43→    level=logging.INFO,\n    44→    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    45→    handlers=[\n    46→        logging.FileHandler(log_dir / \"content-engine.log\"),\n    47→        logging.StreamHandler()\n    48→    ]\n    49→)\n    50→logger = logging.getLogger(__name__)\n    51→\n    52→def load_openai_key():\n    53→    \"\"\"Load OpenAI API key from secrets file.\"\"\"\n    54→    try:\n    55→        secrets_path = \"/root/.clawdbot/secrets.json\"\n    56→        with open(secrets_path, 'r') as f:\n    57→            secrets = json.load(f)\n    58→            return secrets.get(\"openai_api_key\") or secrets.get(\"OPENAI_API_KEY\")\n    59→    except Exception as e:\n    60→        logger.error(f\"Failed to load OpenAI key: {e}\")\n    61→        return None\n    62→\n    63→def encode_image(image_path):\n    64→    \"\"\"Encode image to base64 for API processing.\"\"\"\n    65→    try:\n    66→        if image_path.startswith(('http://', 'https://')):\n    67→            # Download image from URL\n    68→            response = requests.get(image_path, timeout=30)\n    69→            response.raise_for_status()\n    70→            return base64.b64encode(response.content).decode('utf-8')\n    71→        else:\n    72→            # Read local image file\n    73→            with open(image_path, \"rb\") as image_file:\n    74→                return base64.b64encode(image_file.read()).decode('utf-8')\n    75→    except Exception as e:\n    76→        logger.error(f\"Failed to encode image: {e}\")\n    77→        return None\n    78→\n    79→def analyze_construction_photo(image_path):\n    80→    \"\"\"Analyze construction photo using OpenAI Vision API.\"\"\"\n    81→    \n    82→    api_key = load_openai_key()\n    83→    if not api_key:\n    84→        logger.error(\"OpenAI API key not found\")\n    85→        return None\n    86→    \n    87→    client = openai.OpenAI(api_key=api_key)\n    88→    \n    89→    # Encode image\n    90→    base64_image = encode_image(image_path)\n    91→    if not base64_image:\n    92→        return None\n    93→    \n    94→    system_prompt = \"\"\"You are a construction industry expert analyzing site photos for RateRight, \n    95→    an Australian construction hiring marketplace. Provide detailed analysis for marketing purposes.\n    96→    \n    97→    Focus on identifying:\n    98→    1. Trade types and specializations visible\n    99→    2. Safety equipment and compliance\n   100→    3. Equipment, tools, and machinery\n   101→    4. Site conditions and project phase\n   102→    5. Workforce size and composition\n   103→    6. Quality standards and workmanship level\n   104→    \n   105→    Generate marketable insights about hiring needs and workforce requirements.\"\"\"\n   106→\n   107→    user_prompt = \"\"\"Analyze this construction site photo and provide:\n   108→\n   109→1. TRADE IDENTIFICATION\n   110→   - Primary trade(s) visible\n   111→   - Specialization level (apprentice, journeyman, expert)\n   112→   - Number of workers visible\n   113→\n   114→2. SAFETY ASSESSMENT\n   115→   - PPE compliance (hard hats, hi-vis, steel caps, etc.)\n   116→   - Safety equipment present\n   117→   - Any safety concerns visible\n   118→\n   119→3. EQUIPMENT & TOOLS\n   120→   - Major equipment visible\n   121→   - Hand tools and specialized gear\n   122→   - Equipment quality/condition\n   123→\n   124→4. SITE CONDITIONS\n   125→   - Project type (residential, commercial, infrastructure)\n   126→   - Construction phase\n   127→   - Site organization and cleanliness\n   128→   - Weather conditions if apparent\n   129→\n   130→5. MARKETABLE INSIGHTS\n   131→   - Hiring needs this photo suggests\n   132→   - Skill gaps or shortages evident\n   133→   - Equipment/supply needs\n   134→   - Project timeline implications\n   135→\n   136→6. SOCIAL MEDIA CAPTIONS\n   137→   - 3 LinkedIn caption suggestions\n   138→   - 2 Instagram caption suggestions\n   139→   - 1 TikTok caption idea\n   140→   - Include relevant hashtags and emojis where appropriate\n   141→\n   142→Format response with clear sections and bullet points. Be specific and actionable for marketing purposes.\"\"\"\n   143→\n   144→    try:\n   145→        response = client.chat.completions.create(\n   146→            model=\"gpt-4-vision-preview\",\n   147→            messages=[\n   148→                {\"role\": \"system\", \"content\": system_prompt},\n   149→                {\n   150→                    \"role\": \"user\", \n   151→                    \"content\": [\n   152→                        {\"type\": \"text\", \"text\": user_prompt},\n   153→                        {\n   154→                            \"type\": \"image_url\",\n   155→                            \"image_url\": {\n   156→                                \"url\": f\"data:image/jpeg;base64,{base64_image}\"\n   157→                            }\n   158→                        }\n   159→                    ]\n   160→                }\n   161→            ],\n   162→            max_tokens=1500\n   163→        )\n   164→        \n   165→        content = response.choices[0].message.content\n   166→        logger.info(f\"Successfully analyzed construction photo: {image_path}\")\n   167→        return content\n   168→        \n   169→    except Exception as e:\n   170→        logger.error(f\"OpenAI Vision API error: {e}\")\n   171→        return None\n   172→\n   173→def format_output(content, image_path):\n   174→    \"\"\"Format the analysis for display.\"\"\"\n   175→    from datetime import datetime\n   176→    \n   177→    timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n   178→    \n   179→    output = f\"\"\"# RateRight Construction Photo Analyzer\n   180→Analyzed on: {timestamp}\n   181→Image: {image_path}\n   182→\n   183→{'='*60}\n   184→{content}\n   185→{'='*60}\n   186→\n   187→\"\"\"\n   188→    return output\n   189→\n   190→def main():\n   191→    \"\"\"Main function to handle command line execution.\"\"\"\n   192→    \n   193→    if len(sys.argv) < 2:\n   194→        print(\"Usage: python photo-analyzer.py /path/to/photo.jpg\")\n   195→        print(\"Example: python photo-analyzer.py ./site-photos/formwork-crew.jpg\")\n   196→        print(\"         python photo-analyzer.py https://example.com/photo.jpg\")\n   197→        sys.exit(1)\n   198→    \n   199→    image_path = sys.argv[1]\n   200→    logger.info(f\"Starting photo analysis for: {image_path}\")\n   201→    \n   202→    # Validate image path\n   203→    if not image_path.startswith(('http://', 'https://')):\n   204→        if not Path(image_path).exists():\n   205→            print(f\"Error: Image file not found: {image_path}\")\n   206→            logger.error(f\"Image file not found: {image_path}\")\n   207→            sys.exit(1)\n   208→    \n   209→    # Analyze photo\n   210→    content = analyze_construction_photo(image_path)\n   211→    \n   212→    if content:\n   213→        formatted_output = format_output(content, image_path)\n   214→        print(formatted_output)\n   215→        logger.info(\"Photo analysis completed successfully\")\n   216→    else:\n   217→        print(\"Failed to analyze photo. Check logs for details.\")\n   218→        logger.error(\"Photo analysis failed\")\n   219→        sys.exit(1)\n   220→\n   221→if __name__ == \"__main__\":\n   222→    main()\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"7b70523e-ca28-411f-bb94-3880a267e7ee","timestamp":"2026-02-04T19:51:56.155Z","toolUseResult":{"type":"text","file":{"filePath":"/home/ccuser/rateright-growth/rivet/scripts/photo-analyzer.py","content":"#!/usr/bin/env python3\n\"\"\"\nRateRight Construction Photo Analyzer\n\nAnalyze construction site photos to identify trade types, safety gear, equipment, and conditions.\nGenerates marketing insights and post caption suggestions.\n\nUsage:\n    python photo-analyzer.py /path/to/photo.jpg\n    \nExample:\n    python photo-analyzer.py ./site-photos/formwork-crew.jpg\n    python photo-analyzer.py https://example.com/construction-photo.jpg\n\nOutput:\n    - Trade type identification\n    - Safety equipment assessment\n    - Equipment and tools identified\n    - Site conditions evaluation\n    - Marketing insights (e.g., \"steel fixing crew needed\")\n    - Social media caption suggestions\n\"\"\"\n\nimport sys\nimport json\nimport logging\nfrom pathlib import Path\nimport base64\nimport requests\n\ntry:\n    import openai\nexcept ImportError:\n    print(\"OpenAI library not found. Installing...\")\n    import subprocess\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"openai\"])\n    import openai\n\n# Setup logging\nlog_dir = Path(\"/home/ccuser/rateright-growth/rivet/memory\")\nlog_dir.mkdir(exist_ok=True)\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    handlers=[\n        logging.FileHandler(log_dir / \"content-engine.log\"),\n        logging.StreamHandler()\n    ]\n)\nlogger = logging.getLogger(__name__)\n\ndef load_openai_key():\n    \"\"\"Load OpenAI API key from secrets file.\"\"\"\n    try:\n        secrets_path = \"/root/.clawdbot/secrets.json\"\n        with open(secrets_path, 'r') as f:\n            secrets = json.load(f)\n            return secrets.get(\"openai_api_key\") or secrets.get(\"OPENAI_API_KEY\")\n    except Exception as e:\n        logger.error(f\"Failed to load OpenAI key: {e}\")\n        return None\n\ndef encode_image(image_path):\n    \"\"\"Encode image to base64 for API processing.\"\"\"\n    try:\n        if image_path.startswith(('http://', 'https://')):\n            # Download image from URL\n            response = requests.get(image_path, timeout=30)\n            response.raise_for_status()\n            return base64.b64encode(response.content).decode('utf-8')\n        else:\n            # Read local image file\n            with open(image_path, \"rb\") as image_file:\n                return base64.b64encode(image_file.read()).decode('utf-8')\n    except Exception as e:\n        logger.error(f\"Failed to encode image: {e}\")\n        return None\n\ndef analyze_construction_photo(image_path):\n    \"\"\"Analyze construction photo using OpenAI Vision API.\"\"\"\n    \n    api_key = load_openai_key()\n    if not api_key:\n        logger.error(\"OpenAI API key not found\")\n        return None\n    \n    client = openai.OpenAI(api_key=api_key)\n    \n    # Encode image\n    base64_image = encode_image(image_path)\n    if not base64_image:\n        return None\n    \n    system_prompt = \"\"\"You are a construction industry expert analyzing site photos for RateRight, \n    an Australian construction hiring marketplace. Provide detailed analysis for marketing purposes.\n    \n    Focus on identifying:\n    1. Trade types and specializations visible\n    2. Safety equipment and compliance\n    3. Equipment, tools, and machinery\n    4. Site conditions and project phase\n    5. Workforce size and composition\n    6. Quality standards and workmanship level\n    \n    Generate marketable insights about hiring needs and workforce requirements.\"\"\"\n\n    user_prompt = \"\"\"Analyze this construction site photo and provide:\n\n1. TRADE IDENTIFICATION\n   - Primary trade(s) visible\n   - Specialization level (apprentice, journeyman, expert)\n   - Number of workers visible\n\n2. SAFETY ASSESSMENT\n   - PPE compliance (hard hats, hi-vis, steel caps, etc.)\n   - Safety equipment present\n   - Any safety concerns visible\n\n3. EQUIPMENT & TOOLS\n   - Major equipment visible\n   - Hand tools and specialized gear\n   - Equipment quality/condition\n\n4. SITE CONDITIONS\n   - Project type (residential, commercial, infrastructure)\n   - Construction phase\n   - Site organization and cleanliness\n   - Weather conditions if apparent\n\n5. MARKETABLE INSIGHTS\n   - Hiring needs this photo suggests\n   - Skill gaps or shortages evident\n   - Equipment/supply needs\n   - Project timeline implications\n\n6. SOCIAL MEDIA CAPTIONS\n   - 3 LinkedIn caption suggestions\n   - 2 Instagram caption suggestions\n   - 1 TikTok caption idea\n   - Include relevant hashtags and emojis where appropriate\n\nFormat response with clear sections and bullet points. Be specific and actionable for marketing purposes.\"\"\"\n\n    try:\n        response = client.chat.completions.create(\n            model=\"gpt-4-vision-preview\",\n            messages=[\n                {\"role\": \"system\", \"content\": system_prompt},\n                {\n                    \"role\": \"user\", \n                    \"content\": [\n                        {\"type\": \"text\", \"text\": user_prompt},\n                        {\n                            \"type\": \"image_url\",\n                            \"image_url\": {\n                                \"url\": f\"data:image/jpeg;base64,{base64_image}\"\n                            }\n                        }\n                    ]\n                }\n            ],\n            max_tokens=1500\n        )\n        \n        content = response.choices[0].message.content\n        logger.info(f\"Successfully analyzed construction photo: {image_path}\")\n        return content\n        \n    except Exception as e:\n        logger.error(f\"OpenAI Vision API error: {e}\")\n        return None\n\ndef format_output(content, image_path):\n    \"\"\"Format the analysis for display.\"\"\"\n    from datetime import datetime\n    \n    timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n    \n    output = f\"\"\"# RateRight Construction Photo Analyzer\nAnalyzed on: {timestamp}\nImage: {image_path}\n\n{'='*60}\n{content}\n{'='*60}\n\n\"\"\"\n    return output\n\ndef main():\n    \"\"\"Main function to handle command line execution.\"\"\"\n    \n    if len(sys.argv) < 2:\n        print(\"Usage: python photo-analyzer.py /path/to/photo.jpg\")\n        print(\"Example: python photo-analyzer.py ./site-photos/formwork-crew.jpg\")\n        print(\"         python photo-analyzer.py https://example.com/photo.jpg\")\n        sys.exit(1)\n    \n    image_path = sys.argv[1]\n    logger.info(f\"Starting photo analysis for: {image_path}\")\n    \n    # Validate image path\n    if not image_path.startswith(('http://', 'https://')):\n        if not Path(image_path).exists():\n            print(f\"Error: Image file not found: {image_path}\")\n            logger.error(f\"Image file not found: {image_path}\")\n            sys.exit(1)\n    \n    # Analyze photo\n    content = analyze_construction_photo(image_path)\n    \n    if content:\n        formatted_output = format_output(content, image_path)\n        print(formatted_output)\n        logger.info(\"Photo analysis completed successfully\")\n    else:\n        print(\"Failed to analyze photo. Check logs for details.\")\n        logger.error(\"Photo analysis failed\")\n        sys.exit(1)\n\nif __name__ == \"__main__\":\n    main()","numLines":222,"startLine":1,"totalLines":222}},"sourceToolAssistantUUID":"0bc18b47-cf47-41ad-8600-b585edcdcafc"}
{"parentUuid":"7b70523e-ca28-411f-bb94-3880a267e7ee","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","slug":"wild-snacking-clover","message":{"model":"claude-opus-4-5-20251101","id":"msg_013UMT3dWHNKSSCUzQn4WzBi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SmHzGmMQZbEYERjsF9bnn4","name":"Read","input":{"file_path":"/home/ccuser/rateright-growth/rivet/scripts/blog-writer.py"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1568,"cache_read_input_tokens":18142,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1568},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CXocSnZiptB7iTLcyQH63","type":"assistant","uuid":"5e21a284-44f0-4d1e-8980-7e89b4eba178","timestamp":"2026-02-04T19:51:56.533Z"}
{"parentUuid":"5e21a284-44f0-4d1e-8980-7e89b4eba178","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","slug":"wild-snacking-clover","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SmHzGmMQZbEYERjsF9bnn4","type":"tool_result","content":"     1→#!/usr/bin/env python3\n     2→\"\"\"\n     3→RateRight Blog Writer\n     4→\n     5→Generate SEO-optimized blog posts for RateRight website about construction hiring topics.\n     6→\n     7→Usage:\n     8→    python blog-writer.py \"topic/keyword\"\n     9→    \n    10→Example:\n    11→    python blog-writer.py \"how to hire steel fixers in Sydney\"\n    12→    python blog-writer.py \"construction labour hire best practices\"\n    13→\n    14→Output:\n    15→    - 800-1200 word blog post in markdown format\n    16→    - SEO-optimized with construction hiring keywords\n    17→    - Professional but accessible tone\n    18→    - Includes research from web sources\n    19→    - Ready for website publication\n    20→\"\"\"\n    21→\n    22→import sys\n    23→import json\n    24→import logging\n    25→from datetime import datetime\n    26→from pathlib import Path\n    27→\n    28→try:\n    29→    import openai\n    30→except ImportError:\n    31→    print(\"OpenAI library not found. Installing...\")\n    32→    import subprocess\n    33→    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"openai\"])\n    34→    import openai\n    35→\n    36→# Setup logging\n    37→log_dir = Path(\"/home/ccuser/rateright-growth/rivet/memory\")\n    38→log_dir.mkdir(exist_ok=True)\n    39→logging.basicConfig(\n    40→    level=logging.INFO,\n    41→    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    42→    handlers=[\n    43→        logging.FileHandler(log_dir / \"content-engine.log\"),\n    44→        logging.StreamHandler()\n    45→    ]\n    46→)\n    47→logger = logging.getLogger(__name__)\n    48→\n    49→def load_openai_key():\n    50→    \"\"\"Load OpenAI API key from secrets file.\"\"\"\n    51→    try:\n    52→        secrets_path = \"/root/.clawdbot/secrets.json\"\n    53→        with open(secrets_path, 'r') as f:\n    54→            secrets = json.load(f)\n    55→            return secrets.get(\"openai_api_key\") or secrets.get(\"OPENAI_API_KEY\")\n    56→    except Exception as e:\n    57→        logger.error(f\"Failed to load OpenAI key: {e}\")\n    58→        return None\n    59→\n    60→def research_topic(topic):\n    61→    \"\"\"Research topic using web search for current information.\"\"\"\n    62→    try:\n    63→        # Use Brave search to gather current information\n    64→        search_query = f\"construction hiring {topic} Australia Sydney 2024\"\n    65→        \n    66→        # Note: This would use the web_search tool, but since we're in a script,\n    67→        # we'll simulate the research with the topic itself\n    68→        logger.info(f\"Researching topic: {topic}\")\n    69→        \n    70→        # Key areas to research based on the topic\n    71→        research_areas = {\n    72→            \"hiring_challenges\": \"Current construction labour shortages in Sydney\",\n    73→            \"market_trends\": \"Construction industry workforce trends Australia 2024\",\n    74→            \"best_practices\": \"Construction hiring best practices and compliance\",\n    75→            \"local_insights\": \"Sydney construction market conditions and labour demand\"\n    76→        }\n    77→        \n    78→        return f\"\"\"Research findings for: {topic}\n    79→\n    80→Current Market Context (2024):\n    81→- Sydney construction industry facing significant labour shortages\n    82→- High demand for skilled trades: steel fixers, formworkers, concreters\n    83→- Increased focus on safety compliance and certification requirements\n    84→- Growing trend toward specialized labour hire services\n    85→- Project delays due to workforce availability issues\n    86→\n    87→Key Industry Insights:\n    88→- Construction accounts for ~9% of Australian GDP\n    89→- Sydney major infrastructure projects driving demand\n    90→- Skills shortage particularly acute in specialized trades\n    91→- Contractors seeking reliable, compliant labour hire solutions\n    92→- Emphasis on safety record and certification verification\"\"\"\n    93→        \n    94→    except Exception as e:\n    95→        logger.error(f\"Research error: {e}\")\n    96→        return f\"Topic: {topic} - Research data unavailable\"\n    97→\n    98→def generate_blog_post(topic, research_data):\n    99→    \"\"\"Generate blog post using OpenAI API.\"\"\"\n   100→    \n   101→    api_key = load_openai_key()\n   102→    if not api_key:\n   103→        logger.error(\"OpenAI API key not found\")\n   104→        return None\n   105→    \n   106→    client = openai.OpenAI(api_key=api_key)\n   107→    \n   108→    system_prompt = \"\"\"You are a content marketing expert for RateRight, a construction hiring marketplace in Australia.\n   109→    Write SEO-optimized blog posts that rank well in search engines and provide value to contractors and construction professionals.\n   110→    \n   111→    Target Audience: Sydney-based contractors, project managers, construction business owners\n   112→    Tone: Professional but accessible, knowledgeable but not overly technical\n   113→    Style: Educational, actionable, data-driven where possible\n   114→    \n   115→    SEO Requirements:\n   116→    - Target primary keyword in title, first paragraph, and naturally throughout\n   117→    - Include related keywords and long-tail variations\n   118→    - Use header tags (H1, H2, H3) appropriately\n   119→    - Write compelling meta description\n   120→    - Include internal linking opportunities\n   121→    - Optimize for featured snippets when possible\n   122→    \n   123→    Content Structure:\n   124→    - Compelling headline with primary keyword\n   125→    - Engaging introduction that hooks the reader\n   126→    - Clear sections with descriptive headers\n   127→    - Actionable takeaways and tips\n   128→    - Strong conclusion with call-to-action\n   129→    - Include statistics and industry insights\n   130→    \n   131→    Construction Keywords to Include:\n   132→    - construction hiring, labour hire Sydney, steel fixers, formworkers\n   133→    - concreters, scaffolders, carpenters, construction jobs\n   134→    - Sydney construction, Australian construction industry\"\"\"\n   135→\n   136→    user_prompt = f\"\"\"Write a comprehensive blog post about: {topic}\n   137→\n   138→Research Data:\n   139→{research_data}\n   140→\n   141→Requirements:\n   142→- 800-1200 words in markdown format\n   143→- Target primary keyword: {topic}\n   144→- Include relevant construction industry statistics\n   145→- Provide actionable advice for contractors\n   146→- Address common pain points in construction hiring\n   147→- Include RateRight as solution where relevant (but not overly promotional)\n   148→- Use Australian spelling and terminology\n   149→- Include compelling meta description\n   150→- Structure with clear H2 and H3 headers\n   151→- End with strong call-to-action\n   152→\n   153→Format the entire post in markdown with:\n   154→- SEO-optimized title\n   155→- Meta description\n   156→- Table of contents (if appropriate)\n   157→- Clear header hierarchy\n   158→- Bullet points and numbered lists where helpful\n   159→- Bold text for emphasis\n   160→- Internal linking suggestions [in brackets]\"\"\"\n   161→\n   162→    try:\n   163→        response = client.chat.completions.create(\n   164→            model=\"gpt-4\",\n   165→            messages=[\n   166→                {\"role\": \"system\", \"content\": system_prompt},\n   167→                {\"role\": \"user\", \"content\": user_prompt}\n   168→            ],\n   169→            temperature=0.7,\n   170→            max_tokens=3000\n   171→        )\n   172→        \n   173→        content = response.choices[0].message.content\n   174→        logger.info(f\"Successfully generated blog post for topic: {topic}\")\n   175→        return content\n   176→        \n   177→    except Exception as e:\n   178→        logger.error(f\"OpenAI API error: {e}\")\n   179→        return None\n   180→\n   181→def format_blog_output(content, topic):\n   182→    \"\"\"Format the blog post for display.\"\"\"\n   183→    timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n   184→    \n   185→    output = f\"\"\"# RateRight Blog Writer\n   186→Generated on: {timestamp}\n   187→Topic: {topic}\n   188→Word Count: ~{len(content.split())} words\n   189→\n   190→{'='*60}\n   191→{content}\n   192→{'='*60}\n   193→\n   194→\"\"\"\n   195→    return output\n   196→\n   197→def main():\n   198→    \"\"\"Main function to handle command line execution.\"\"\"\n   199→    \n   200→    if len(sys.argv) < 2:\n   201→        print(\"Usage: python blog-writer.py 'topic/keyword'\")\n   202→        print(\"Example: python blog-writer.py 'how to hire steel fixers in Sydney'\")\n   203→        print(\"         python blog-writer.py 'construction labour hire best practices'\")\n   204→        sys.exit(1)\n   205→    \n   206→    topic = \" \".join(sys.argv[1:])\n   207→    logger.info(f\"Starting blog post generation for topic: {topic}\")\n   208→    \n   209→    # Research topic\n   210→    research_data = research_topic(topic)\n   211→    logger.info(\"Research phase completed\")\n   212→    \n   213→    # Generate blog post\n   214→    content = generate_blog_post(topic, research_data)\n   215→    \n   216→    if content:\n   217→        formatted_output = format_blog_output(content, topic)\n   218→        print(formatted_output)\n   219→        logger.info(\"Blog post generated successfully\")\n   220→        \n   221→        # Save to file option\n   222→        save_option = input(\"Save to file? (y/n): \").lower().strip()\n   223→        if save_option == 'y':\n   224→            filename = f\"blog-{topic.replace(' ', '-').lower()[:50]}.md\"\n   225→            output_path = Path(\"/home/ccuser/rateright-growth/rivet/scripts\") / filename\n   226→            \n   227→            with open(output_path, 'w', encoding='utf-8') as f:\n   228→                f.write(content)\n   229→            \n   230→            print(f\"Blog post saved to: {output_path}\")\n   231→            logger.info(f\"Blog post saved to: {output_path}\")\n   232→            \n   233→    else:\n   234→        print(\"Failed to generate blog post. Check logs for details.\")\n   235→        logger.error(\"Blog post generation failed\")\n   236→        sys.exit(1)\n   237→\n   238→if __name__ == \"__main__\":\n   239→    main()\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"841aa84f-1f29-4d51-8ba4-5c127882deb9","timestamp":"2026-02-04T19:51:56.538Z","toolUseResult":{"type":"text","file":{"filePath":"/home/ccuser/rateright-growth/rivet/scripts/blog-writer.py","content":"#!/usr/bin/env python3\n\"\"\"\nRateRight Blog Writer\n\nGenerate SEO-optimized blog posts for RateRight website about construction hiring topics.\n\nUsage:\n    python blog-writer.py \"topic/keyword\"\n    \nExample:\n    python blog-writer.py \"how to hire steel fixers in Sydney\"\n    python blog-writer.py \"construction labour hire best practices\"\n\nOutput:\n    - 800-1200 word blog post in markdown format\n    - SEO-optimized with construction hiring keywords\n    - Professional but accessible tone\n    - Includes research from web sources\n    - Ready for website publication\n\"\"\"\n\nimport sys\nimport json\nimport logging\nfrom datetime import datetime\nfrom pathlib import Path\n\ntry:\n    import openai\nexcept ImportError:\n    print(\"OpenAI library not found. Installing...\")\n    import subprocess\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"openai\"])\n    import openai\n\n# Setup logging\nlog_dir = Path(\"/home/ccuser/rateright-growth/rivet/memory\")\nlog_dir.mkdir(exist_ok=True)\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    handlers=[\n        logging.FileHandler(log_dir / \"content-engine.log\"),\n        logging.StreamHandler()\n    ]\n)\nlogger = logging.getLogger(__name__)\n\ndef load_openai_key():\n    \"\"\"Load OpenAI API key from secrets file.\"\"\"\n    try:\n        secrets_path = \"/root/.clawdbot/secrets.json\"\n        with open(secrets_path, 'r') as f:\n            secrets = json.load(f)\n            return secrets.get(\"openai_api_key\") or secrets.get(\"OPENAI_API_KEY\")\n    except Exception as e:\n        logger.error(f\"Failed to load OpenAI key: {e}\")\n        return None\n\ndef research_topic(topic):\n    \"\"\"Research topic using web search for current information.\"\"\"\n    try:\n        # Use Brave search to gather current information\n        search_query = f\"construction hiring {topic} Australia Sydney 2024\"\n        \n        # Note: This would use the web_search tool, but since we're in a script,\n        # we'll simulate the research with the topic itself\n        logger.info(f\"Researching topic: {topic}\")\n        \n        # Key areas to research based on the topic\n        research_areas = {\n            \"hiring_challenges\": \"Current construction labour shortages in Sydney\",\n            \"market_trends\": \"Construction industry workforce trends Australia 2024\",\n            \"best_practices\": \"Construction hiring best practices and compliance\",\n            \"local_insights\": \"Sydney construction market conditions and labour demand\"\n        }\n        \n        return f\"\"\"Research findings for: {topic}\n\nCurrent Market Context (2024):\n- Sydney construction industry facing significant labour shortages\n- High demand for skilled trades: steel fixers, formworkers, concreters\n- Increased focus on safety compliance and certification requirements\n- Growing trend toward specialized labour hire services\n- Project delays due to workforce availability issues\n\nKey Industry Insights:\n- Construction accounts for ~9% of Australian GDP\n- Sydney major infrastructure projects driving demand\n- Skills shortage particularly acute in specialized trades\n- Contractors seeking reliable, compliant labour hire solutions\n- Emphasis on safety record and certification verification\"\"\"\n        \n    except Exception as e:\n        logger.error(f\"Research error: {e}\")\n        return f\"Topic: {topic} - Research data unavailable\"\n\ndef generate_blog_post(topic, research_data):\n    \"\"\"Generate blog post using OpenAI API.\"\"\"\n    \n    api_key = load_openai_key()\n    if not api_key:\n        logger.error(\"OpenAI API key not found\")\n        return None\n    \n    client = openai.OpenAI(api_key=api_key)\n    \n    system_prompt = \"\"\"You are a content marketing expert for RateRight, a construction hiring marketplace in Australia.\n    Write SEO-optimized blog posts that rank well in search engines and provide value to contractors and construction professionals.\n    \n    Target Audience: Sydney-based contractors, project managers, construction business owners\n    Tone: Professional but accessible, knowledgeable but not overly technical\n    Style: Educational, actionable, data-driven where possible\n    \n    SEO Requirements:\n    - Target primary keyword in title, first paragraph, and naturally throughout\n    - Include related keywords and long-tail variations\n    - Use header tags (H1, H2, H3) appropriately\n    - Write compelling meta description\n    - Include internal linking opportunities\n    - Optimize for featured snippets when possible\n    \n    Content Structure:\n    - Compelling headline with primary keyword\n    - Engaging introduction that hooks the reader\n    - Clear sections with descriptive headers\n    - Actionable takeaways and tips\n    - Strong conclusion with call-to-action\n    - Include statistics and industry insights\n    \n    Construction Keywords to Include:\n    - construction hiring, labour hire Sydney, steel fixers, formworkers\n    - concreters, scaffolders, carpenters, construction jobs\n    - Sydney construction, Australian construction industry\"\"\"\n\n    user_prompt = f\"\"\"Write a comprehensive blog post about: {topic}\n\nResearch Data:\n{research_data}\n\nRequirements:\n- 800-1200 words in markdown format\n- Target primary keyword: {topic}\n- Include relevant construction industry statistics\n- Provide actionable advice for contractors\n- Address common pain points in construction hiring\n- Include RateRight as solution where relevant (but not overly promotional)\n- Use Australian spelling and terminology\n- Include compelling meta description\n- Structure with clear H2 and H3 headers\n- End with strong call-to-action\n\nFormat the entire post in markdown with:\n- SEO-optimized title\n- Meta description\n- Table of contents (if appropriate)\n- Clear header hierarchy\n- Bullet points and numbered lists where helpful\n- Bold text for emphasis\n- Internal linking suggestions [in brackets]\"\"\"\n\n    try:\n        response = client.chat.completions.create(\n            model=\"gpt-4\",\n            messages=[\n                {\"role\": \"system\", \"content\": system_prompt},\n                {\"role\": \"user\", \"content\": user_prompt}\n            ],\n            temperature=0.7,\n            max_tokens=3000\n        )\n        \n        content = response.choices[0].message.content\n        logger.info(f\"Successfully generated blog post for topic: {topic}\")\n        return content\n        \n    except Exception as e:\n        logger.error(f\"OpenAI API error: {e}\")\n        return None\n\ndef format_blog_output(content, topic):\n    \"\"\"Format the blog post for display.\"\"\"\n    timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n    \n    output = f\"\"\"# RateRight Blog Writer\nGenerated on: {timestamp}\nTopic: {topic}\nWord Count: ~{len(content.split())} words\n\n{'='*60}\n{content}\n{'='*60}\n\n\"\"\"\n    return output\n\ndef main():\n    \"\"\"Main function to handle command line execution.\"\"\"\n    \n    if len(sys.argv) < 2:\n        print(\"Usage: python blog-writer.py 'topic/keyword'\")\n        print(\"Example: python blog-writer.py 'how to hire steel fixers in Sydney'\")\n        print(\"         python blog-writer.py 'construction labour hire best practices'\")\n        sys.exit(1)\n    \n    topic = \" \".join(sys.argv[1:])\n    logger.info(f\"Starting blog post generation for topic: {topic}\")\n    \n    # Research topic\n    research_data = research_topic(topic)\n    logger.info(\"Research phase completed\")\n    \n    # Generate blog post\n    content = generate_blog_post(topic, research_data)\n    \n    if content:\n        formatted_output = format_blog_output(content, topic)\n        print(formatted_output)\n        logger.info(\"Blog post generated successfully\")\n        \n        # Save to file option\n        save_option = input(\"Save to file? (y/n): \").lower().strip()\n        if save_option == 'y':\n            filename = f\"blog-{topic.replace(' ', '-').lower()[:50]}.md\"\n            output_path = Path(\"/home/ccuser/rateright-growth/rivet/scripts\") / filename\n            \n            with open(output_path, 'w', encoding='utf-8') as f:\n                f.write(content)\n            \n            print(f\"Blog post saved to: {output_path}\")\n            logger.info(f\"Blog post saved to: {output_path}\")\n            \n    else:\n        print(\"Failed to generate blog post. Check logs for details.\")\n        logger.error(\"Blog post generation failed\")\n        sys.exit(1)\n\nif __name__ == \"__main__\":\n    main()","numLines":239,"startLine":1,"totalLines":239}},"sourceToolAssistantUUID":"5e21a284-44f0-4d1e-8980-7e89b4eba178"}
{"parentUuid":"841aa84f-1f29-4d51-8ba4-5c127882deb9","isSidechain":false,"userType":"external","cwd":"/home/ccuser/rateright-growth/rivet/scripts","sessionId":"b739dfda-f181-4126-8abd-5b86e740b0c3","version":"2.1.31","gitBranch":"main","slug":"wild-snacking-clover","message":{"model":"claude-opus-4-5-20251101","id":"msg_01MP9op1TiuvoPUGnioCC8Y2","type":"message","role":"assistant","content":[{"type":"text","text":"---\n\n## Code Review: Content Engine Scripts\n\n---\n\n### 1. `linkedin-generator.py`\n\n**Issues**\n\n| Severity | Issue |\n|----------|-------|\n| **Critical** | **Auto-installs packages at import time** (lines 27-33). Runs `pip install` as a side effect of importing. In production this is a supply chain risk — a compromised PyPI package could be installed silently. Dependencies should be declared in `requirements.txt`. |\n| **Critical** | **Hardcoded secrets path** (line 51). `/root/.clawdbot/secrets.json` ties the script to a single machine and user. Fails silently elsewhere. Should use env vars or a configurable path. |\n| **Medium** | **Bare `except Exception`** throughout. Catches everything including `KeyboardInterrupt` in Python <3.11 edge cases. More targeted exceptions (`FileNotFoundError`, `openai.APIError`) would improve debuggability. |\n| **Medium** | **No input sanitization** on `topic` (line 83). User input is injected directly into the prompt. While not a traditional injection, prompt injection via CLI args is possible. |\n| **Minor** | **No output persistence** — generated content is only printed, never saved. Easy to lose work. |\n| **Minor** | **No retry logic** for API calls. A single transient failure kills the run. |\n\n**Quality Score: 5/10** — Functional prototype, not production-ready.\n\n---\n\n### 2. `photo-analyzer.py`\n\n**Issues**\n\n| Severity | Issue |\n|----------|-------|\n| **Critical** | **SSRF via URL input** (line 68). `requests.get(image_path)` fetches any user-supplied URL with no allowlist/validation. An attacker could target internal services (`http://169.254.169.254/` for cloud metadata, `http://localhost:*`). |\n| **Critical** | **Auto-install at import** (lines 31-36). Same supply chain risk as above. |\n| **Critical** | **Hardcoded secrets path** (line 55). Same issue. |\n| **Critical** | **Deprecated model** (line 147). `gpt-4-vision-preview` is deprecated. Should use `gpt-4o` or `gpt-4-turbo`. |\n| **Medium** | **No file size limit** on image reads (line 73). `open(image_path, \"rb\").read()` on a multi-GB file will OOM the process. |\n| **Medium** | **No file type validation**. Accepts any file extension — PDFs, executables, etc. will be base64-encoded and sent to the API, wasting tokens. |\n| **Medium** | **`requests` imported but not declared** as a dependency. Will fail if not installed. |\n| **Minor** | **`datetime` imported inside function** (line 175) instead of at module level — inconsistent with the rest of the script. |\n\n**Quality Score: 3/10** — Has real security vulnerabilities (SSRF). Not safe for production.\n\n---\n\n### 3. `blog-writer.py`\n\n**Issues**\n\n| Severity | Issue |\n|----------|-------|\n| **Critical** | **Auto-install at import** (lines 29-33). Same issue across all three scripts. |\n| **Critical** | **Hardcoded secrets path** (line 53). Same issue. |\n| **Critical** | **`research_topic()` is a fake** (lines 60-96). The function claims to \"research topic using web search\" but returns hardcoded static text every time. The \"research\" is a lie — the blog posts are generated from canned data, not real research. The hardcoded year \"2024\" will also be stale. |\n| **Medium** | **Interactive `input()` call** (line 222) in what could be a scripted/automated pipeline. Blocks indefinitely if run non-interactively (cron, CI, piped input). Should use a CLI flag (`--save`) instead. |\n| **Medium** | **Unsanitized filename construction** (line 224). `topic.replace(' ', '-').lower()[:50]` doesn't strip path traversal characters. A topic like `../../etc/cron.d/evil` would write outside the intended directory. |\n| **Medium** | **No input sanitization** on topic passed to prompt. |\n| **Minor** | **Word count is approximate** (line 188). `len(content.split())` is displayed as exact but prefixed with `~`, which is fine, but misleading in a \"production\" output. |\n\n**Quality Score: 3/10** — Fake research function and path traversal make this unreliable and unsafe.\n\n---\n\n### Cross-Cutting Recommendations\n\n1. **Eliminate auto-install pattern** in all three scripts. Use a `requirements.txt` and install deps at deploy time.\n2. **Use environment variables** (`OPENAI_API_KEY`) instead of hardcoded secrets paths. The OpenAI client reads `OPENAI_API_KEY` from env by default — no custom loading needed.\n3. **Add input validation** — sanitize CLI args, validate file types/sizes, restrict URLs to public hosts.\n4. **Extract shared code** — `load_openai_key()`, logging setup, and the auto-install block are copy-pasted identically across all three files. Should be a shared module.\n5. **Add retry logic** with exponential backoff for API calls (use `tenacity` or `backoff` library).\n6. **Fix the fake research function** in `blog-writer.py` — either implement real web search or remove the misleading wrapper.\n7. **Fix the SSRF** in `photo-analyzer.py` — validate URLs against an allowlist, block private/internal IPs.\n8. **Update deprecated model** `gpt-4-vision-preview` → `gpt-4o`.\n\n---\n\n### Summary\n\n| Script | Score | Status |\n|--------|-------|--------|\n| `linkedin-generator.py` | **5/10** | Functional prototype, needs hardening |\n| `photo-analyzer.py` | **3/10** | SSRF vulnerability, deprecated model, not production-safe |\n| `blog-writer.py` | **3/10** | Fake research, path traversal risk, blocking `input()` |\n\nAll three are in \"quick prototype\" state — fine for local testing, not ready for production or automation pipelines."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":7763,"cache_read_input_tokens":19710,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":7763},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CXocT5sXMaRzcmKfDJXm9","type":"assistant","uuid":"6033d422-4042-45f4-a81f-177df3323d99","timestamp":"2026-02-04T19:52:30.868Z"}