Daily AI outfit recommendations based on weather forecast to Slack
Description Start your day with the perfect outfit suggestion tailored to the local weather. This workflow runs automatically every morning, fetches the current weather forecast for your city, and uses an AI stylist to generate a practical, gender-neutral outfit recommendation. It then designs a clean, vertical image card with all the details—date, temperature, weather conditions, and the complete outfit advice—and posts it directly to your Slack channel. It’s like having a personal stylist and weather reporter deliver a daily briefing right where your team communicates. Who’s it for Teams working in a shared office location who want a fun, daily update. Individuals looking to automate their morning routine and take the guesswork out of getting dressed. Community managers wanting to add engaging, automated content to their Slack workspace. Anyone interested in a practical example of combining weather data, AI, and dynamic image generation. How it works / What it does Triggers Daily: The workflow automatically runs every day at 6 AM. Fetches Weather: It gets the current weather forecast for a specified city (default is Tokyo) using the OpenWeatherMap node. Consults AI Stylist: The weather data is sent to an AI model, which acts as a stylist and returns a practical, gender-neutral outfit suggestion. Designs an Image Card: It dynamically creates a vertical image and writes the date, detailed weather info, and the AI's full recommendation onto it. Posts to Slack: Finally, it uploads the completed image card to your designated Slack channel with a friendly morning greeting. Requirements An n8n instance. An OpenWeatherMap API Key. An OpenRouter API Key (or credentials for another compatible AI model). A Slack workspace and the necessary permissions to connect an app. How to set up Set Weather Location: In the Get Weather Data node, add your OpenWeatherMap API Key and change the city name if you wish. Configure AI Model: In the OpenRouter Chat Model node, add your API Key. Configure Slack: In the Upload a file node, add your Slack credentials and, most importantly, select the channel where you want the forecast to be posted. Adjust Schedule (Optional): You can change the trigger time in the Daily 6AM Trigger node. How to customize the workflow Change the AI's Personality: Edit the system message in the Generate Outfit Advice node. You could ask the AI to be a pirate, a 90s fashion icon, or a formal stylist. Customize the Image: In the Create Image Card node, you can change the background color, font sizes, colors, and the layout of the text. Use a Different Platform: Swap the Slack node for a Discord, Telegram, or Email node to send the forecast to your preferred platform.
Daily AI Outfit Recommendations Based on Weather Forecast to Slack
This n8n workflow automates the process of generating personalized daily outfit recommendations based on the local weather forecast and delivering them directly to a Slack channel. It leverages AI to interpret weather data and suggest suitable attire, simplifying your morning routine.
What it does
- Schedules Daily Trigger: The workflow runs automatically at a predefined schedule (e.g., every morning).
- Fetches Weather Forecast: It retrieves the current weather forecast for a specified location using OpenWeatherMap.
- Processes Weather Data: The raw weather data is then processed and formatted.
- Generates AI Outfit Recommendation: An AI agent (powered by a Chat Language Model like OpenRouter) analyzes the weather conditions (temperature, precipitation, wind, etc.) and generates a detailed outfit recommendation.
- Formats Output: The AI-generated recommendation is prepared for display.
- Posts to Slack: The final outfit recommendation is posted as a message to a designated Slack channel.
Prerequisites/Requirements
- n8n Instance: A running n8n instance.
- OpenWeatherMap API Key: An API key for OpenWeatherMap to fetch weather data.
- AI Agent Configuration:
- An AI Agent node configured with a suitable Chat Language Model (e.g., OpenRouter).
- OpenRouter API Key: An API key for OpenRouter or your chosen LLM provider.
- Slack Account & API Token: A Slack workspace and an API token for a bot user with permissions to post messages to a channel.
Setup/Usage
- Import the Workflow:
- Download the provided JSON file.
- In your n8n instance, click on "Workflows" in the left sidebar.
- Click "New Workflow" or "Import from JSON" if you have an existing workflow.
- Paste the JSON content into the import dialog.
- Configure Credentials:
- OpenWeatherMap:
- Click on the "OpenWeatherMap" node.
- Under "Credentials", click "Create New" or select an existing one.
- Enter your OpenWeatherMap API Key.
- AI Agent (OpenRouter Chat Model):
- Click on the "OpenRouter Chat Model" node (within the "AI Agent" node if it's nested).
- Under "Credentials", click "Create New" or select an existing one.
- Enter your OpenRouter API Key.
- Slack:
- Click on the "Slack" node.
- Under "Credentials", click "Create New" or select an existing one.
- Provide your Slack Bot User OAuth Token.
- OpenWeatherMap:
- Configure Nodes:
- Schedule Trigger: Adjust the schedule to your preference (e.g., daily at 7 AM).
- OpenWeatherMap:
- Set the
CityorCoordinatesfor which you want to get the weather forecast. - Choose your preferred
Units(e.g., metric, imperial).
- Set the
- AI Agent:
- Review the prompt provided to the AI agent to ensure it aligns with your desired outfit recommendation style. You might want to customize it to include specific preferences (e.g., "professional attire," "casual," "sporty").
- Slack:
- Specify the
Channelwhere the outfit recommendations should be posted (e.g.,#daily-outfits).
- Specify the
- Activate the Workflow:
- Toggle the workflow to "Active" in the top right corner of the n8n editor.
The workflow will now run according to its schedule, providing you with AI-generated outfit recommendations based on the latest weather forecast directly in Slack.
Related Templates
Automate RSS to social media pipeline with AI, Airtable & GetLate for multiple platforms
Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (to-share-linkedin, to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags social-media-automation, ai-content-generation, rss-to-social, multi-platform-posting, getlate-api, airtable-database, langchain, workflow-automation, content-marketing
Ai website scraper & company intelligence
AI Website Scraper & Company Intelligence Description This workflow automates the process of transforming any website URL into a structured, intelligent company profile. It's triggered by a form, allowing a user to submit a website and choose between a "basic" or "deep" scrape. The workflow extracts key information (mission, services, contacts, SEO keywords), stores it in a structured Supabase database, and archives a full JSON backup to Google Drive. It also features a secondary AI agent that automatically finds and saves competitors for each company, building a rich, interconnected database of company intelligence. --- Quick Implementation Steps Import the Workflow: Import the provided JSON file into your n8n instance. Install Custom Community Node: You must install the community node from: https://www.npmjs.com/package/n8n-nodes-crawl-and-scrape FIRECRAWL N8N Documentation https://docs.firecrawl.dev/developer-guides/workflow-automation/n8n Install Additional Nodes: n8n-nodes-crawl-and-scrape and n8n-nodes-mcp fire crawl mcp . Set up Credentials: Create credentials in n8n for FIRE CRAWL API,Supabase, Mistral AI, and Google Drive. Configure API Key (CRITICAL): Open the Web Search tool node. Go to Parameters → Headers and replace the hardcoded Tavily AI API key with your own. Configure Supabase Nodes: Assign your Supabase credential to all Supabase nodes. Ensure table names (e.g., companies, competitors) match your schema. Configure Google Drive Nodes: Assign your Google Drive credential to the Google Drive2 and save to Google Drive1 nodes. Select the correct Folder ID. Activate Workflow: Turn on the workflow and open the Webhook URL in the “On form submission” node to access the form. --- What It Does Form Trigger Captures user input: “Website URL” and “Scraping Type” (basic or deep). Scraping Router A Switch node routes the flow: Deep Scraping → AI-based MCP Firecrawler agent. Basic Scraping → Crawlee node. Deep Scraping (Firecrawl AI Agent) Uses Firecrawl and Tavily Web Search. Extracts a detailed JSON profile: mission, services, contacts, SEO keywords, etc. Basic Scraping (Crawlee) Uses Crawl and Scrape node to collect raw text. A Mistral-based AI extractor structures the data into JSON. Data Storage Stores structured data in Supabase tables (companies, company_basicprofiles). Archives a full JSON backup to Google Drive. Automated Competitor Analysis Runs after a deep scrape. Uses Tavily web search to find competitors (e.g., from Crunchbase). Saves competitor data to Supabase, linked by company_id. --- Who's It For Sales & Marketing Teams: Enrich leads with deep company info. Market Researchers: Build structured, searchable company databases. B2B Data Providers: Automate company intelligence collection. Developers: Use as a base for RAG or enrichment pipelines. --- Requirements n8n instance (self-hosted or cloud) Supabase Account: With tables like companies, competitors, social_links, etc. Mistral AI API Key Google Drive Credentials Tavily AI API Key (Optional) Custom Nodes: n8n-nodes-crawl-and-scrape --- How It Works Flow Summary Form Trigger: Captures “Website URL” and “Scraping Type”. Switch Node: deep → MCP Firecrawler (AI Agent). basic → Crawl and Scrape node. Scraping & Extraction: Deep path: Firecrawler → JSON structure. Basic path: Crawlee → Mistral extractor → JSON. Storage: Save JSON to Supabase. Archive in Google Drive. Competitor Analysis (Deep Only): Finds competitors via Tavily. Saves to Supabase competitors table. End: Finishes with a No Operation node. --- How To Set Up Import workflow JSON. Install community nodes (especially n8n-nodes-crawl-and-scrape from npm). Configure credentials (Supabase, Mistral AI, Google Drive). Add your Tavily API key. Connect Supabase and Drive nodes properly. Fix disconnected “basic” path if needed. Activate workflow. Test via the webhook form URL. --- How To Customize Change LLMs: Swap Mistral for OpenAI or Claude. Edit Scraper Prompts: Modify system prompts in AI agent nodes. Change Extraction Schema: Update JSON Schema in extractor nodes. Fix Relational Tables: Add Items node before Supabase inserts for arrays (social links, keywords). Enhance Automation: Add email/slack notifications, or replace form trigger with a Google Sheets trigger. --- Add-ons Automated Trigger: Run on new sheet rows. Notifications: Email or Slack alerts after completion. RAG Integration: Use the Supabase database as a chatbot knowledge source. --- Use Case Examples Sales Lead Enrichment: Instantly get company + competitor data from a URL. Market Research: Collect and compare companies in a niche. B2B Database Creation: Build a proprietary company dataset. --- WORKFLOW IMAGE --- Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|-----------| | Form Trigger 404 | Workflow not active | Activate the workflow | | Web Search Tool fails | Missing Tavily API key | Replace the placeholder key | | FIRECRAWLER / find competitor fails | Missing MCP node | Install n8n-nodes-mcp | | Basic scrape does nothing | Switch node path disconnected | Reconnect “basic” output | | Supabase node error | Wrong table/column names | Match schema exactly | --- Need Help or More Workflows? Want to customize this workflow for your business or integrate it with your existing tools? Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements. Contact: shilpa.raju@digitalbiz.tech For more such offerings, visit us: https://www.digitalbiz.tech ---
Automated YouTube video uploads with 12h interval scheduling in JST
This workflow automates a batch upload of multiple videos to YouTube, spacing each upload 12 hours apart in Japan Standard Time (UTC+9) and automatically adding them to a playlist. ⚙️ Workflow Logic Manual Trigger — Starts the workflow manually. List Video Files — Uses a shell command to find all .mp4 files under the specified directory (/opt/downloads/单词卡/A1-A2). Sort and Generate Items — Sorts videos by day number (dayXX) extracted from filenames and assigns a sequential order value. Calculate Publish Schedule (+12h Interval) — Computes the next rounded JST hour plus a configurable buffer (default 30 min). Staggers each video’s scheduled time by order × 12 hours. Converts JST back to UTC for YouTube’s publishAt field. Split in Batches (1 per video) — Iterates over each video item. Read Video File — Loads the corresponding video from disk. Upload to YouTube (Scheduled) — Uploads the video privately with the computed publishAtUtc. Add to Playlist — Adds the newly uploaded video to the target playlist. 🕒 Highlights Timezone-safe: Pure UTC ↔ JST conversion avoids double-offset errors. Sequential scheduling: Ensures each upload is 12 hours apart to prevent clustering. Customizable: Change SPANHOURS, BUFFERMIN, or directory paths easily. Retry-ready: Each upload and playlist step has retry logic to handle transient errors. 💡 Typical Use Cases Multi-part educational video series (e.g., A1–A2 English learning). Regular content release cadence without manual scheduling. Automated YouTube publishing pipelines for pre-produced content. --- Author: Zane Category: Automation / YouTube / Scheduler Timezone: JST (UTC+09:00)