Back to Catalog

Add a song to your Spotify queue

eem2188eem2188
1317 views
2/3/2026
Official Page

workflow-screenshot

Add a Song to Your Spotify Queue

This n8n workflow provides a basic template for interacting with the Spotify API. While the current version is a minimal "Start" node followed by a "Spotify" node, it serves as an excellent foundation for building more complex Spotify automations.

What it does

This workflow, in its current state, performs the following steps:

  1. Starts the workflow: The Start node initiates the execution of the workflow.
  2. Initializes Spotify interaction: The Spotify node is ready to be configured for various Spotify actions.

Prerequisites/Requirements

  • n8n instance: A running n8n instance to import and execute the workflow.
  • Spotify Account: A Spotify account is required to create credentials and interact with the Spotify API.
  • Spotify API Credentials: You will need to set up Spotify API credentials (Client ID and Client Secret) within n8n to authenticate with Spotify.

Setup/Usage

  1. Import the workflow: Import the provided JSON into your n8n instance.
  2. Configure Spotify Credentials:
    • Click on the Spotify node.
    • In the node settings, click "Create New Credential" or select an existing Spotify credential.
    • Follow the instructions to authenticate your Spotify account with n8n. This typically involves getting a Client ID and Client Secret from the Spotify Developer Dashboard and authorizing n8n.
  3. Expand Functionality:
    • The Spotify node is currently in its default state. To add a song to your queue, you would need to configure the Spotify node further.
    • Common actions for adding a song to a queue would involve:
      • Searching for a track: Use the "Search" operation to find a song by title and artist.
      • Getting track URI: Extract the URI of the desired track from the search results.
      • Adding to queue: Use the "Add Track to Playback Queue" operation, providing the track URI.
    • You might also want to add input nodes (e.g., a "Webhook" or "Manual Trigger" with parameters) to specify the song to be added.
  4. Activate the workflow: Once configured, activate the workflow to make it ready for execution.

Related Templates

AI-powered code review with linting, red-marked corrections in Google Sheets & Slack

Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.

higashiyama By higashiyama
90

Generate Weather-Based Date Itineraries with Google Places, OpenRouter AI, and Slack

🧩 What this template does This workflow builds a 120-minute local date course around your starting point by querying Google Places for nearby spots, selecting the top candidates, fetching real-time weather data, letting an AI generate a matching emoji, and drafting a friendly itinerary summary with an LLM in both English and Japanese. It then posts the full bilingual plan with a walking route link and weather emoji to Slack. 👥 Who it’s for Makers and teams who want a plug-and-play bilingual local itinerary generator with weather awareness — no custom code required. ⚙️ How it works Trigger – Manual (or schedule/webhook). Discovery – Google Places nearby search within a configurable radius. Selection – Rank by rating and pick the top 3. Weather – Fetch current weather (via OpenWeatherMap). Emoji – Use an AI model to match the weather with an emoji 🌤️. Planning – An LLM writes the itinerary in Markdown (JP + EN). Route – Compose a Google Maps walking route URL. Share – Post the bilingual itinerary, route link, and weather emoji to Slack. 🧰 Requirements n8n (Cloud or self-hosted) Google Maps Platform (Places API) OpenWeatherMap API key Slack Bot (chat:write) LLM provider (e.g., OpenRouter or DeepL for translation) 🚀 Setup (quick) Open Set → Fields: Config and fill in coords/radius/time limit. Connect Credentials for Google, OpenWeatherMap, Slack, and your LLM. Test the workflow and confirm the bilingual plan + weather emoji appear in Slack. 🛠 Customize Adjust ranking filters (type, min rating). Modify translation settings (target language or tone). Change output layout (side-by-side vs separated). Tune emoji logic or travel mode. Add error handling, retries, or logging for production use.

nodaBy noda
52

AI-powered document search with Oracle and ONNX embeddings for recruiting

How it works Create a user for doing Hybrid Search. Clear Existing Data, if present. Add Documents into the table. Create a hybrid index. Run Semantic search on the Documents table for "prioritize teamwork and leadership experience". Run Hybrid search for the text input in the Chat interface on the Documents table. Setup Steps Download the ONNX model allMiniLML12v2augmented.zip Extract the ZIP file on the database server into a directory, for example /opt/oracle/onnx. After extraction, the folder contents should look like: bash bash-4.4$ pwd /opt/oracle/onnx bash-4.4$ ls allMiniLML12_v2.onnx Connect as SYSDBA and create the DBA user sql -- Create DBA user CREATE USER app_admin IDENTIFIED BY "StrongPassword123" DEFAULT TABLESPACE users TEMPORARY TABLESPACE temp QUOTA UNLIMITED ON users; -- Grant privileges GRANT DBA TO app_admin; GRANT CREATE TABLESPACE, ALTER TABLESPACE, DROP TABLESPACE TO app_admin; Create n8n Oracle DB credentials hybridsearchuser → for hybrid search operations dbadocuser → for DBA setup (user and tablespace creation) Run the workflow Click the manual Trigger It displays Pure semantic search results. Enter search text in Chat interface It displays results for vector and keyword search. Note The workflow currently creates the hybrid search user, docuser with the password visible in plain text inside the n8n Execute SQL node. For better security, consider performing the user creation manually outside n8n. Oracle 23ai or 26ai Database has to be used. Reference Hybrid Search End-End Example

sudarshanBy sudarshan
211