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    "name": "Trakt.tv Integration",
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    "steps": [
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      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Trakt.tv Integration for OpenClaw",
        "body": "Integrate with Trakt.tv to track watch history and provide personalized show/movie recommendations.\n\n📚 Trakt API Documentation: https://trakt.docs.apiary.io/"
      },
      {
        "title": "First-Time Setup Required",
        "body": "Before using this skill, run the interactive setup:"
      },
      {
        "title": "Automated Setup (Recommended)",
        "body": "python3 scripts/setup.py\n\nThis will guide you through:\n\nInstalling dependencies\nCreating a Trakt application\nConfiguring credentials\nAuthenticating with PIN\nTesting the integration"
      },
      {
        "title": "Manual Setup",
        "body": "If automated setup doesn't work, follow the manual steps in the Setup section below."
      },
      {
        "title": "Interactive Setup for OpenClaw",
        "body": "When a user asks to \"install Trakt\" or \"set up Trakt integration,\" OpenClaw should:\n\nRead INSTALL.md for detailed interactive flow\nOr run python3 scripts/setup.py and guide user through prompts"
      },
      {
        "title": "Features",
        "body": "Track watch history (automatically synced by Trakt from streaming services)\nGet personalized recommendations based on viewing habits\nAccess user watchlists and collections\nSearch for shows and movies\nView trending content"
      },
      {
        "title": "Prerequisites",
        "body": "Python dependencies:\n# Install via pip (with --break-system-packages if needed)\npip3 install requests\n\n# OR use a virtual environment (recommended)\npython3 -m venv ~/.openclaw-venv\nsource ~/.openclaw-venv/bin/activate\npip install requests\n\nAlternatively, install via Homebrew if available:\nbrew install python-requests\n\n\n\nTrakt.tv account with Pro subscription (required for automatic watch tracking)\n\n\nTrakt API application - Create at https://trakt.tv/oauth/applications\n\n\nConfiguration file: ~/.openclaw/trakt_config.json (see setup below)"
      },
      {
        "title": "1. Create Trakt Application",
        "body": "Visit https://trakt.tv/oauth/applications\nClick \"New Application\"\nFill in the form:\n\nName: \"OpenClaw Assistant\"\nDescription: \"Personal AI assistant integration\"\nRedirect URI: urn:ietf:wg:oauth:2.0:oob (for PIN auth)\nPermissions: Check all that apply\n\n\nSave and note your Client ID and Client Secret"
      },
      {
        "title": "2. Create Configuration File",
        "body": "Create ~/.openclaw/trakt_config.json with your credentials:\n\n{\n  \"client_id\": \"YOUR_CLIENT_ID_HERE\",\n  \"client_secret\": \"YOUR_CLIENT_SECRET_HERE\",\n  \"access_token\": \"\",\n  \"refresh_token\": \"\"\n}\n\nReplace YOUR_CLIENT_ID_HERE and YOUR_CLIENT_SECRET_HERE with your actual values from step 1.\n\nNote: Leave access_token and refresh_token empty - they'll be filled automatically after authentication."
      },
      {
        "title": "3. Authenticate",
        "body": "Run the authentication script:\n\npython3 scripts/trakt_client.py auth\n\nThis will output a PIN URL. Visit it, authorize the app, and run:\n\npython3 scripts/trakt_client.py auth <PIN>\n\nAuthentication tokens are saved to ~/.openclaw/trakt_config.json"
      },
      {
        "title": "Get Recommendations",
        "body": "When a user asks for show/movie recommendations:\n\npython3 scripts/trakt_client.py recommend\n\nThis returns personalized recommendations based on the user's watch history and ratings."
      },
      {
        "title": "Check Watch History",
        "body": "python3 scripts/trakt_client.py history\n\nReturns the user's recent watch history."
      },
      {
        "title": "View Watchlist",
        "body": "python3 scripts/trakt_client.py watchlist\n\nShows content the user has saved to watch later."
      },
      {
        "title": "Search",
        "body": "python3 scripts/trakt_client.py search \"Breaking Bad\"\n\nSearch for specific shows or movies."
      },
      {
        "title": "Trending Content",
        "body": "python3 scripts/trakt_client.py trending\n\nGet currently trending shows and movies."
      },
      {
        "title": "Recommendation Workflow",
        "body": "When a user asks \"What should I watch?\" or similar:\n\nGet personalized recommendations:\npython3 scripts/trakt_client.py recommend\n\n\n\nParse the results and present them naturally:\n\nShow title, year, rating\nBrief description/genre\nWhy it's recommended (if available)\n\n\n\nOptionally check watchlist to avoid suggesting shows they already plan to watch\n\n\nConsider recent history to avoid re-suggesting recently watched content"
      },
      {
        "title": "API Reference",
        "body": "See references/api.md for detailed Trakt API endpoint documentation."
      },
      {
        "title": "Common Use Cases",
        "body": "\"What should I watch tonight?\"\n\nGet recommendations, filter by mood/genre if specified\nCheck trending if user wants something popular\n\n\"Add [show] to my watchlist\"\n\nSearch for the show\nAdd to Trakt watchlist (requires additional endpoint implementation)\n\n\"What have I been watching lately?\"\n\nGet watch history\nSummarize recent shows/movies\n\n\"Is [show] trending?\"\n\nGet trending list\nSearch for specific show"
      },
      {
        "title": "Limitations",
        "body": "Trakt Pro subscription required for automatic watch tracking from streaming services\nRecommendations improve over time as watch history grows\nAPI rate limits apply: 1000 requests per 5 minutes (authenticated)\nFull API documentation: https://trakt.docs.apiary.io/"
      },
      {
        "title": "Troubleshooting",
        "body": "\"Authentication failed\"\n\nVerify CLIENT_ID and CLIENT_SECRET are set correctly in ~/.openclaw/trakt_config.json\nEnsure PIN is copied accurately (case-sensitive)\nCheck that your Trakt application has proper permissions\n\n\"No recommendations returned\"\n\nUser may not have enough watch history yet\nTry falling back to trending content\nEnsure user has rated some content on Trakt\n\n\"API request failed\"\n\nCheck authentication token hasn't expired\nVerify network connectivity\nCheck Trakt API status: https://status.trakt.tv"
      }
    ],
    "body": "Trakt.tv Integration for OpenClaw\n\nIntegrate with Trakt.tv to track watch history and provide personalized show/movie recommendations.\n\n📚 Trakt API Documentation: https://trakt.docs.apiary.io/\n\nFirst-Time Setup Required\n\nBefore using this skill, run the interactive setup:\n\nAutomated Setup (Recommended)\npython3 scripts/setup.py\n\n\nThis will guide you through:\n\nInstalling dependencies\nCreating a Trakt application\nConfiguring credentials\nAuthenticating with PIN\nTesting the integration\nManual Setup\n\nIf automated setup doesn't work, follow the manual steps in the Setup section below.\n\nInteractive Setup for OpenClaw\n\nWhen a user asks to \"install Trakt\" or \"set up Trakt integration,\" OpenClaw should:\n\nRead INSTALL.md for detailed interactive flow\nOr run python3 scripts/setup.py and guide user through prompts\nFeatures\nTrack watch history (automatically synced by Trakt from streaming services)\nGet personalized recommendations based on viewing habits\nAccess user watchlists and collections\nSearch for shows and movies\nView trending content\nPrerequisites\n\nPython dependencies:\n\n# Install via pip (with --break-system-packages if needed)\npip3 install requests\n\n# OR use a virtual environment (recommended)\npython3 -m venv ~/.openclaw-venv\nsource ~/.openclaw-venv/bin/activate\npip install requests\n\n\nAlternatively, install via Homebrew if available:\n\nbrew install python-requests\n\n\nTrakt.tv account with Pro subscription (required for automatic watch tracking)\n\nTrakt API application - Create at https://trakt.tv/oauth/applications\n\nConfiguration file: ~/.openclaw/trakt_config.json (see setup below)\n\nSetup\n1. Create Trakt Application\nVisit https://trakt.tv/oauth/applications\nClick \"New Application\"\nFill in the form:\nName: \"OpenClaw Assistant\"\nDescription: \"Personal AI assistant integration\"\nRedirect URI: urn:ietf:wg:oauth:2.0:oob (for PIN auth)\nPermissions: Check all that apply\nSave and note your Client ID and Client Secret\n2. Create Configuration File\n\nCreate ~/.openclaw/trakt_config.json with your credentials:\n\n{\n  \"client_id\": \"YOUR_CLIENT_ID_HERE\",\n  \"client_secret\": \"YOUR_CLIENT_SECRET_HERE\",\n  \"access_token\": \"\",\n  \"refresh_token\": \"\"\n}\n\n\nReplace YOUR_CLIENT_ID_HERE and YOUR_CLIENT_SECRET_HERE with your actual values from step 1.\n\nNote: Leave access_token and refresh_token empty - they'll be filled automatically after authentication.\n\n3. Authenticate\n\nRun the authentication script:\n\npython3 scripts/trakt_client.py auth\n\n\nThis will output a PIN URL. Visit it, authorize the app, and run:\n\npython3 scripts/trakt_client.py auth <PIN>\n\n\nAuthentication tokens are saved to ~/.openclaw/trakt_config.json\n\nUsage\nGet Recommendations\n\nWhen a user asks for show/movie recommendations:\n\npython3 scripts/trakt_client.py recommend\n\n\nThis returns personalized recommendations based on the user's watch history and ratings.\n\nCheck Watch History\npython3 scripts/trakt_client.py history\n\n\nReturns the user's recent watch history.\n\nView Watchlist\npython3 scripts/trakt_client.py watchlist\n\n\nShows content the user has saved to watch later.\n\nSearch\npython3 scripts/trakt_client.py search \"Breaking Bad\"\n\n\nSearch for specific shows or movies.\n\nTrending Content\npython3 scripts/trakt_client.py trending\n\n\nGet currently trending shows and movies.\n\nRecommendation Workflow\n\nWhen a user asks \"What should I watch?\" or similar:\n\nGet personalized recommendations:\n\npython3 scripts/trakt_client.py recommend\n\n\nParse the results and present them naturally:\n\nShow title, year, rating\nBrief description/genre\nWhy it's recommended (if available)\n\nOptionally check watchlist to avoid suggesting shows they already plan to watch\n\nConsider recent history to avoid re-suggesting recently watched content\n\nAPI Reference\n\nSee references/api.md for detailed Trakt API endpoint documentation.\n\nCommon Use Cases\n\n\"What should I watch tonight?\"\n\nGet recommendations, filter by mood/genre if specified\nCheck trending if user wants something popular\n\n\"Add [show] to my watchlist\"\n\nSearch for the show\nAdd to Trakt watchlist (requires additional endpoint implementation)\n\n\"What have I been watching lately?\"\n\nGet watch history\nSummarize recent shows/movies\n\n\"Is [show] trending?\"\n\nGet trending list\nSearch for specific show\nLimitations\nTrakt Pro subscription required for automatic watch tracking from streaming services\nRecommendations improve over time as watch history grows\nAPI rate limits apply: 1000 requests per 5 minutes (authenticated)\nFull API documentation: https://trakt.docs.apiary.io/\nTroubleshooting\n\n\"Authentication failed\"\n\nVerify CLIENT_ID and CLIENT_SECRET are set correctly in ~/.openclaw/trakt_config.json\nEnsure PIN is copied accurately (case-sensitive)\nCheck that your Trakt application has proper permissions\n\n\"No recommendations returned\"\n\nUser may not have enough watch history yet\nTry falling back to trending content\nEnsure user has rated some content on Trakt\n\n\"API request failed\"\n\nCheck authentication token hasn't expired\nVerify network connectivity\nCheck Trakt API status: https://status.trakt.tv"
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