This article provides a complete, practical walkthrough of the Splitout Code Monitor Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automated Spotify Listening Archive and AI-Powered Playlist Curation with n8n and Claude 3.5
   
   Meta Description:  
   Discover how to automate monthly Spotify listening history archiving and intelligent playlist curation using an advanced n8n workflow powered by Claude 3.5 and Spotify/Google Sheets integrations.
   
   Keywords:  
   Spotify automation, music workflow, n8n tutorial, AI playlist classification, Claude 3.5, Spotify API, Google Sheets integration, Anthropic, music archiving, custom playlists
   
   Third-Party APIs Used:
   
   1. Spotify API (OAuth2)  
   2. Google Sheets API (OAuth2)  
   3. Anthropic Claude 3.5 API via LangChain integration
   
   Short Article:
   
   For music lovers and data enthusiasts alike, organizing personal listening history while curating intelligent playlists can be both valuable and cumbersome. If you've ever wished there were an easier way to catalog your monthly Spotify favorites and effortlessly sort them into genre, mood, or era-specific playlists, this n8n workflow is a game-changer.
   
   Introducing an advanced no-code automation that combines Spotify's API, Google Sheets' flexible data storage, and Claude 3.5's natural language AI—this robust setup does more than just log your tracks. It classifies them contextually into tailored playlists that evolve with your music taste.
   
   🧠 Why Automate Spotify Archiving?
   
   Manual archiving may be nostalgic but it's time-consuming. The key objectives of this workflow are:
   - Automatically log newly liked Spotify tracks on a monthly basis.
   - Store rich metadata including audio features and classification details in Google Sheets.
   - Use Claude 3.5 (via LangChain) to intelligently classify newly added tracks into user-curated playlists.
   - Avoid duplicate entries and update playlists seamlessly.
   
   🛠️ Workflow Breakdown
   
   Here’s a simplified overview of the workflow’s key components:
   
   1. Monthly Trigger
      - Initiated either via a schedule or manually, this node triggers the process to fetch your Spotify data.
   
   2. Fetch Spotify Tracks and Playlists
      - The workflow uses the Spotify API to retrieve:
        - All tracks from your library
        - Your created playlists (filtered by owner "Arnaud")
   
   3. Clean & Enhance Track Data
      - Tracks are enriched with Spotify’s audio-feature data (like danceability, tempo, and valence) for better classification.
      - Metadata such as artist, URI, album, release year, and popularity is also included.
   
   4. Filter Out Already Logged Tracks
      - Google Sheets integration ensures that duplicate songs aren’t reprocessed.
      - Only new additions proceed further for classification and playlisting.
   
   5. Log New Tracks to Google Sheets
      - A new row is appended for each track that hasn't been previously logged, ensuring an ever-growing archive.
   
   6. Update Playlist Metadata
      - If a playlist is new, its name, description, and URI are also logged in a separate sheet.
   
   7. AI Classification with Claude 3.5
      - One of the most powerful features: Claude 3.5 receives detailed track data along with playlist descriptions.
      - Using fine-tuned prompts and natural language reasoning, the AI sorts each track into the most relevant playlists. A track can belong to multiple playlists like "Classique" and "To Sing" if applicable.
   
   8. Update Spotify Playlists
      - Based on Claude's results, the workflow updates your Spotify playlists in batches, respecting API limits.
      - Tracks are added only if they aren’t already listed in the selected playlist.
   
   🧠 Claude 3.5’s Role Explained
   
   Claude 3.5 Sonnet is called through LangChain’s @n8n/n8n-nodes-langchain module. The model examines input data like instrumentality, acousticness, tempo, artist name, and more to conduct deeper classification. Playlist description examples (like “Workout Motivation” or “Psytrance”) provide semantic context for improved accuracy.
   
   Sample Claude Output (for one playlist assignment):
   
   ```json
   {
     "playlistName": "Cumbia",
     "uri": "spotify:playlist:1SwaCdO1tS2BbF8IL3WwXO",
     "trackUris": [
       "spotify:track:6QsovprLkdGeE9FSsOjuQA",
       "spotify:track:3hH0sVIoIoPOTmMdjmXSob",
       "spotify:track:4Sd525AYAaYuiexGHTcoFy"
     ]
   }
   ```
   
   💡 Use Cases
   
   - Music historians looking to archive listening behavior.
   - Audiophiles wanting to automate playlist management.
   - Artists or DJs organizing tracks by mood, genre, or composition style.
   - Data enthusiasts applying AI models to personal media classification.
   
   💸 Cost & Performance
   
   The average batch of 300 tracks requires approx. 60,000 tokens (~20 cents in October 2024 based on Claude 3.5 Sonnet pricing). For most users, that's an excellent cost-to-value ratio considering the automation and intelligence.
   
   🔧 Customization Suggestions
   
   - Add more auditory metrics (like key or time signature) for genre-specific filtering.
   - Integrate Notion or Airtable for deeper track/playlist journaling.
   - Use manual verification steps (already scaffolded in the template) to review Claude’s output before updates.
   
   🎯 Final Thoughts
   
   This end-to-end n8n workflow reimagines how we catalog and curate music. With Spotify, Google Sheets, and Claude 3.5 working in harmony, your music life becomes smarter, faster, and more organized—without lifting a finger every month.
   
   Whether you're building the ultimate workout mix, refining your French Chanson collection, or diving deep into 1980s nostalgia, this workflow ensures your musical world stays beautifully synced.
   
   Ready to redefine how you interact with your music? This template might just be the harmonic convergence your playlist routine needed. 🎵

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.