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Monitor Docker host health via SSH with GPT-4o-mini and alerts to Discord

This n8n template builds an automated health monitoring dashboard for your homelab Docker host. It SSHs into your server, collects 30+ system and container metrics, analyzes trends with AI, and delivers a structured multi-embed dashboard to Discord -- plus real-time critical alerts when things go wrong. Stop SSHing into your server every morning to check if everything's still running. AI reads your metrics and tells you exactly what needs attention, with copy-paste fix commands. Good to know Estimated cost is ~$0.003 per daily run using GPT-4o-mini. Compatible with Claude or any OpenAI-compatible LLM -- swap the model sub-node to switch providers. See the setup notes inside the workflow for Claude configuration. Uses Google Sheets for 7-day metric history and trend analysis. A one-click setup trigger auto-creates a formatted tracking spreadsheet with frozen headers and conditional formatting. The critical alert path runs every 5 minutes with a lightweight check (configurable). The daily digest runs once in the morning. Both schedules are adjustable. How it works Daily schedule trigger SSHs into your Docker host and collects system metrics (real CPU % from /proc/stat, memory, all filesystems, swap, network I/O, top processes, zombie processes, failed services) and Docker metrics (container status, CPU, memory, restarts, health checks, disk usage, dangling images) in ~2 seconds A 100-point health score is calculated from weighted metrics across CPU, memory, disk, swap, containers, and system health 7 days of historical data is loaded from Google Sheets for trend comparison AI analyzes current vs. historical metrics and returns structured JSON with severity-tagged findings, CLI fix commands, trend analysis, and a top recommendation A 4-embed Discord dashboard is delivered: status header with inline metrics, actionable findings, Docker ecosystem overview with trends, and a footer with timing and API cost Today's metrics are stored in Google Sheets for future trend tracking A separate lightweight path runs every 5 minutes checking critical thresholds (disk > 90%, memory > 95%, inodes > 90%, containers down) and fires immediate alerts How to use Click "Test workflow" on the first-time setup trigger to auto-create your Google Sheets tracking dashboard Copy the Sheet ID into the configuration node, add your Discord webhook URL, wire your SSH and OpenAI credentials, and activate Full setup guides linked inside the workflow for SSH keys, API keys, and Discord webhooks Requirements SSH access to a Linux Docker host (key-based authentication) (ssh key setup) OpenAI API key or Anthropic API key (OpenAI setup guide | Claude setup guide) Google Sheets OAuth2 credential (n8n docs) Discord webhook URL (setup guide) Customizing this workflow Adjust alert thresholds in the configuration node (disk warning/critical, memory warning/critical, inode critical, restart threshold) Change the daily digest and critical alert schedules in the trigger nodes Swap OpenAI for Claude or Ollama by replacing the LLM sub-node Replace Discord with Slack, Telegram, or ntfy by modifying the webhook payload format Add additional SSH metrics by editing the collection commands

BeginnerManual Trigger

by nXsi

Create AI social media carousels from Google Sheet and auto-publish with Blotato

🚀 Create Pro-Level Social Media Carousels & Auto-Publish with Blotato By @nocodehack Who is this for? This workflow is built for e-commerce brands, social media managers, marketing agencies, dropshippers, content creators, and automation builders who need to produce professional carousel posts at scale. Perfect for anyone running product marketing, brand campaigns, multi-platform social media, affiliate content, or any business that publishes carousel posts regularly and wants to eliminate design costs entirely. What problem is this workflow solving? / Use case Creating professional carousel posts is: Slow** — designing even one carousel takes 30-60 minutes manually Expensive** — Fiverr/Upwork designers charge $50-100 per carousel Inconsistent** — AI-generated slides never visually match each other Unscalable** — managing multiple brands multiplies every problem Tedious** — exporting, uploading, scheduling, and publishing is repetitive busywork This workflow solves: ❌ Manual carousel design (Canva, Photoshop, Figma) ❌ Paying designers per post ❌ AI images that look obviously AI-generated ❌ Visually inconsistent slides that don't match ❌ Manual copywriting for captions and hashtags ❌ Manual uploading and publishing to each platform ❌ Managing multiple brands with different visual identities It turns one Google Sheet row into a fully designed, published carousel — across Instagram, Facebook, and X — for approximately 5 cents. What this workflow does This automation system acts as a complete AI-powered carousel design studio and publishing pipeline. Step-by-step pipeline: Step 1 — Data Pipeline (Google Sheet) Runs on a schedule (configurable interval) Pulls the next unprocessed row from Google Sheets Each row = one carousel (one brand, one product, one post) Marks the row as "Processing" to prevent duplicate execution Checks if product description and images are provided — if missing, auto-scrapes from the product URL using Jina.ai (free, no account needed) Merges all data into one clean payload for the AI Step 2 — AI Creative Direction (Claude) Sends all product data (description, images as base64, brand logo, creative specifications) to Claude via the Anthropic API Claude acts as an executive creative director — not just generating content, but building a complete visual identity first: Color palette (2-3 hex colors) Typography style and hierarchy Lighting direction and mood Signature design element Background texture concept Then generates for each slide: headline, body copy, layout approach, and a detailed 80+ word image prompt A 2000-word system prompt with banned elements list eliminates the generic AI look (no waves, no scattered leaves, no flat backgrounds, no Canva-style templates) Every image prompt ends with a negative prompt / AVOID block — same concept as Stable Diffusion negative prompts, applied to Gemini Output is structured JSON via a parser — no freeform text that could break the pipeline Also generates the Instagram caption and hashtags Step 3 — Image Generation with Visual Consistency Loop This is the core innovation of the workflow** Slides are generated sequentially, NOT in parallel — this is critical For slide 1: Gemini generates the image from the prompt + product reference images For slide 2+: The workflow fetches all previously generated slides, converts them to base64, and attaches them as reference images alongside the current prompt The text prompt explicitly instructs: "Match the exact typography, color palette, and lighting from the attached previous slides" This creates a double enforcement system — visual reference + written instruction Result: every slide in the carousel shares the same visual identity without using templates or presets Images are generated via NanoBanana Pro (Gemini image generation API) Each generated slide is uploaded to Blotato media storage and saved to a global memory array for the next iteration Uses $getWorkflowStaticData('global') to persist slide URLs across loop iterations Step 4 — Publishing & Status Update Collects all uploaded slide URLs in correct order Reads the "Socials" field from the Google Sheet (comma-separated: instagram, facebook, x) Routes to the correct platform via a Switch node Publishes via Blotato API — supports immediate posting or scheduled posting (ISO 8601 format) One row can publish to all three platforms simultaneously Updates the Google Sheet row: Status → "Published" + direct Post URL If anything breaks: Status → "Failed" with error details ➡️ Result: One Google Sheet row in, one fully designed and published multi-platform carousel out. 5 cents. 5 minutes. Setup Required accounts & API keys: Google Sheets** — read/write access to your content spreadsheet Anthropic** — Claude API key (creative direction + copywriting) Google AI / Gemini** — API key for image generation via NanoBanana Pro ($300 free credit per new Gmail) Blotato** — API key for media upload + multi-platform publishing Jina.ai** — free web scraping, no account required (10M tokens free) Google Sheet structure: Column Description Brand Logo URL Direct link to your brand logo — placed on every slide automatically Product URL Link to product page — used for auto-scraping if description/images are empty Product Description Optional — write it yourself for best results, or leave blank to auto-scrape Product Images URL Direct links to product photos (comma-separated for multiple) Specification Creative direction hint (e.g. "dark cinematic luxury" or "bright playful minimal") — leave empty for AI to decide Post Date YYYY-MM-DD format — workflow only picks up rows matching today's date Post Hour now for immediate publish, or 14:00 / 2pm for scheduled Socials Comma-separated platforms: instagram, facebook, x Status Leave empty — auto-filled: Processing → Published / Failed Post URL Leave empty — auto-filled with direct link to live post Configuration steps: Import the workflow JSON into n8n Add all required API credentials in n8n's credential manager Create your Google Sheet using the template provided (link in resources) Set your Blotato profile IDs in each publishing node (one per platform) Map platform outputs in the Switch node Verify the Gemini image generation endpoint in the NanoBanana Pro node Test with one row before activating production mode Recommended hosting: n8n is free and open source but needs a server. A VPS with at least 2GB RAM handles image generation and multiple API calls without issues. The workflow runs 24/7 on schedule. How to customize Change AI model:** Swap Claude for GPT-4o or Gemini in the LLM Chain node — the structured output parser works with any model Change slide count:** Edit the system prompt and user prompt (currently locked to 3 slides) Change visual style:** Edit the creative direction in the system prompt — modify banned elements, change composition approaches, adjust the quality standard Add platforms:** Add new outputs to the Switch node + new Blotato publish nodes (Blotato supports TikTok, LinkedIn, Pinterest, Threads, YouTube, Bluesky) Add approval step:** Insert a Wait node before publishing to manually review before posting Change image hosting:** Swap Blotato Upload for Cloudinary or any S3-compatible storage Change scraper:** Swap Jina.ai for any other web scraping tool Adjust scheduling:** Modify the Schedule Trigger interval and use the Post Hour column for per-post timing Multi-brand setup:** Each row can have a different brand logo and creative specification — the AI generates a fresh visual identity per row Cost breakdown per carousel (approx.) Component Cost Claude (creative direction + copy, ~8K tokens) ~$0.02 Gemini (3 slide images via NanoBanana Pro) ~$0.03 Jina.ai (web scraping) Free Blotato (publishing) Per plan Total per carousel ~$0.05 Compare: Fiverr/Upwork designers charge $50-100 per carousel post. This workflow does it for 5 cents. Gemini offers $300 free credit per new Gmail account — enough for thousands of carousels before spending anything. Expected outcome You get a fully automated carousel production system that can: Generate agency-quality carousel designs from a spreadsheet Maintain visual consistency across all slides without templates Handle multiple brands with completely different visual identities Publish to Instagram, Facebook, and X simultaneously Schedule content weeks in advance Scale from 1 carousel/day to dozens without additional effort Eliminate design costs almost entirely Typical use cases E-commerce product marketing (daily product carousels) Brand awareness campaigns across multiple platforms Affiliate marketing content at scale Social media agency client deliverables Dropshipping product promotion Multi-brand social media management Content calendar automation A/B testing different creative directions for the same product Watch the full step-by-step walkthrough. 🎥 Video Tutorial 👋 Need help or want to customize? 📩 Contact: LinkedIn 📺 YouTube: @nocodehack 🌐 Resources & Downloads: nocodehack.io

Discover SaaS creators from HubSpot with influencers.club, OpenAI and Gmail/SendGrid

How it works: Get multi social platform data for SaaS clients with their email and send personalized comms to onboard them as organic creators, partners and ambassadors. Step by step workflow to enrich customer emails with multi social (Instagram, Tiktok, Youtube, Twitter, Onlyfans, Twitch and more) profiles, analytics and metrics using the influencers.club API and sending tailored outreach to activate them as creators. Set up: Hubspot (can be swapped for any CRM like Salesforce, Attio or DB) Influencers.club API key Gmail Sendgrid (can be swapped for any programmatic email sender like Mailgun)

IntermediateManual Trigger

Qualify mortgage leads and collect documents with Gemini, Gmail, Drive, Telegram, and Supabase

This workflow serves as a complete "AI Receptionist" for mortgage brokers or high-ticket service providers. It automates the messy process of qualifying leads, getting internal approval, and collecting sensitive client documents securely. The workflow is fully annotated with Sticky Notes to help you understand every step of the logic. Key Features Smart Qualification:** automatically filters leads based on income thresholds (e.g., >$80k) before they reach your inbox. Human-in-the-Loop:** Uses Telegram buttons to ask the broker for "Approve/Decline" confirmation before generating client emails. Generative AI:* Uses *Google Gemini (2.5-flash-lite)** to write personalized, warm welcome emails based on the lead's specific loan type. Google Workspace Automation:** Automatically creates a unique Google Drive folder for the client and drafts the email in Gmail (with the upload link pre-inserted). Document Collection Portal:** Includes a validation loop that checks if the client uploaded all required files (ID, Payslip, Bank Statements) and notifies the broker of success or failure. Database Logging:** Keeps a "Source of Truth" log in Supabase for every lead action. Prerequisites To use this template, you will need credentials for: Google Cloud Console: Enable APIs for Drive, Gmail, and Gemini (PaLM). Telegram: A Bot Token and Chat ID for notifications. Supabase: A project URL and API Key (Database schema provided in the Sticky Notes). How to Configure Credentials: Set up your Google, Telegram, and Supabase credentials in n8n. Supabase Table: Create a table named leads_consolidated (schema details are in the workflow notes). Triggers: Ensure your n8n instance is publicly accessible so the Webhook and Form triggers function correctly. Telegram ID: Update the Chat ID fields in the Telegram nodes to your own ID. This template is designed to be modular—you can easily swap Supabase for Airtable or Gemini for OpenAI if preferred.

Intermediate

Turn your portfolio into an AI assistant with OpenAI, Claude, Pinecone and Gmail

Deploy a personal AI assistant that answers recruiter questions about your skills and projects, then automatically emails your CV as a PDF attachment when requested. Upload your portfolio documents (resume, project writeups, case studies) to a Google Drive folder — the workflow chunks them into 600-character segments, embeds them with OpenAI, and stores them in Pinecone. A webhook-powered AI Agent (Claude Sonnet 4.5) retrieves the most relevant evidence using Pinecone + Cohere reranking, detects CV requests via structured output parsing, and sends your resume file via Gmail — all without any manual intervention. How it works Ingestion pipeline: Two Google Drive poll triggers fire every minute, detecting newly created or updated files in your monitored portfolio folder Files are downloaded and enriched with metadata (source filename and upload timestamp) The Default Data Loader extracts text from the binary file, the Recursive Character Text Splitter chunks it at 600 characters with 100-character overlap, and OpenAI text-embedding-3-small produces 1536-dimension vectors Vectors are upserted into the portfolio-docs Pinecone index Chat agent pipeline: A webhook at POST /webhook/portfolio-query receives { "chatInput": "...", "sessionId": "...", "email": "..." } Claude Sonnet 4.5 is instructed to call the portfolio_knowledge tool (a Vector Store Tool backed by Pinecone) before answering — every response is grounded in retrieved evidence Cohere rerank-v3.5 reranks the top-5 Pinecone results to top-3 before they reach the LLM A Structured Output Parser enforces { "answer": "...", "cvRequested": false } — the cvRequested boolean is set by the LLM when it detects recruiter intent An IF node branches on cvRequested: true → download CV PDF from Drive → Gmail attachment → respond { answer, cvSent: true }; false → respond { answer, cvSent: false } immediately Buffer Window Memory retains the last 10 messages per sessionId for multi-turn conversations Error handling: An Error Trigger catches any node failure and extracts error_message, failed_node, workflow_name, and execution_url into a clean object — ready to forward to Slack, email, or any alerting webhook. Use cases Job seekers & freelancers** — A 24/7 recruiter-ready assistant that answers questions about your experience and sends your CV on request, even while you sleep Portfolio websites** — A backend API endpoint that powers intelligent Q&A on your personal site without building custom infrastructure Consultants & agencies** — Adapt the ingestion pipeline for a client-facing knowledge base; swap Gmail for any email or messaging node Setup Prerequisites A Pinecone account with an index named portfolio-docs (dimension: 1536, metric: cosine, pod or serverless) A Google Drive folder containing your portfolio documents (PDF, DOCX, or plain text) Your CV stored as a PDF in Google Drive (note its file ID) n8n instance with the six credentials below configured Step 1 — Configure credentials In n8n → Settings → Credentials, create one credential for each service: | Credential name | Service | |---|---| | Google Drive OAuth2 | Google Drive OAuth2 | | OpenAI API | OpenAI | | Pinecone API | Pinecone | | Anthropic API | Anthropic | | Cohere API | Cohere | | Gmail OAuth2 | Gmail OAuth2 | Step 2 — Set your Google Drive folder ID Open File Created Trigger and File Updated Trigger (do both). In the Folder to Watch field, switch to ID mode and paste your folder ID. > Find your folder ID in the Drive URL: https://drive.google.com/drive/folders/YOUR_FOLDER_ID Step 3 — Set your CV file ID Open Download CV PDF. In the File field, switch to ID mode and paste your CV file ID. > Find the file ID in: https://drive.google.com/file/d/YOUR_FILE_ID/view Step 4 — Personalize the system prompt Open Portfolio AI Agent and edit the system message: Replace the generic role description with your name and specialization Adjust the tone (formal/casual) to match how you want to present yourself Update the call-to-action line to reference your actual contact details Step 5 — Ingest your documents Move or upload your portfolio files into the monitored Drive folder. The ingestion pipeline will trigger automatically within one minute and populate Pinecone. To verify ingestion: check your Pinecone index vector count — it should increase after each file is processed. Step 6 — Test the chat endpoint Send a POST request to your webhook URL: curl -X POST https://your-n8n-instance/webhook/portfolio-query \ -H "Content-Type: application/json" \ -d '{"chatInput": "What are your main technical skills?", "sessionId": "test-1"}' Expected response: { "answer": "...", "cvSent": false } To test CV delivery, include "email": "your@email.com" and ask: "Can you send me the CV?". Workflow details Nodes**: 25 functional nodes + 4 documentation sticky notes Triggers**: 4 (2 Google Drive poll triggers, 1 Webhook, 1 Error Trigger) AI components**: Claude Sonnet 4.5 (×2 — agent + retrieval), OpenAI text-embedding-3-small (×2 — ingest + retrieval), Cohere rerank-v3.5, Pinecone Vector Store (×2 — insert + retrieve), Structured Output Parser, Buffer Window Memory Canvas layout**: Three clearly labelled sections with grey sticky note backgrounds — Ingestion pipeline, AI Agent pipeline, Error Handling

Get enriched location, weather, and timezone data using free APIs

Get location insights using free APIs Transform GPS coordinates into rich location data using 100% free APIs. This webhook aggregates address, timezone, weather, and sun data into a single response. Perfect for location tracking apps, travel platforms, and IoT projects. What You'll Get A single API endpoint returning 28 enriched data fields: Detailed address with components (suburb, city, state, country, postcode) Timezone data (name, abbreviation, current local time and date) Live weather (temperature, humidity, pressure, conditions with icon) Sun times (sunrise, sunset, day length) Visual assets (weather icons and country flag URLs) How It Works Webhook receives coordinates via GET request (lat and lon parameters) Parallel API calls to 4 free services (OpenStreetMap, TimezoneDB, Sunrise-Sunset, OpenWeatherMap) Data merging combines all responses Format and structure transforms data into 28 clean fields JSON response returns enriched location data Response time: 1 to 3 seconds Set Up Steps 1. Get Free API Keys OpenWeatherMap: Sign up at openweathermap.org (free tier: 60 calls/min) TimezoneDB: Register at timezonedb.com (free tier available) Note: OpenStreetMap and Sunrise-Sunset require no keys 2. Configure Credentials Add OpenWeatherMap credentials to the OpenWeatherMap node Replace TimezoneDB API key in HTTP Request node with your key 3. Activate and Test Activate workflow to generate webhook URL Test with sample: ?lat=27.1751495&lon=78.0395673 Example Usage Request: curl "https://your-n8n.com/webhook/geo-details?lat=27.1751495&lon=78.0395673" Response: JSON array with 28 fields including address, timezone, weather, and sun data. Use Cases Location tracking applications Travel and tourism platforms Weather dashboards Fleet management systems Geographic analytics tools Smart home automation Educational projects Why This Template? Zero cost (all APIs are free) Production ready with error handling Fast response via parallel processing Well documented with sticky notes Easy to customize and extend No vendor lock-in Perfect for startups, indie developers, students, and budget-conscious projects. Rate Limits (Free Tiers) OpenStreetMap: 1 req/sec TimezoneDB: 1 req/sec OpenWeatherMap: 60 calls/min, 1M calls/month Sunrise-Sunset: No documented limits Tip: Implement caching to stay within limits. Tags: location, geocoding, weather, timezone, free-api, webhook, rest-api, gps

Store variables between workflow runs using data tables as a key-value store

Store Variables Between Workflow Runs Using n8n's Built-In Data Tables (Key-Value Store) Purpose This workflow implements a simple key value store based on the built-in data tables feature in n8n – no external service needed. What this is for This is a workflow snippet which could be used to store a “last checked” timestamp or other variables between workflow runs. Setup Self-deploying – no extra setup needed Just clone the template into your n8n instance and follow the instructions on the yellow sticky notes How it works A data table is used as a key value store having the two columns “key” and “value”. It is referenced by name, which makes it easier to migrate this workflow to other instances. At the beginning a variable is retrieved by it’s key If the variable / key does not exist yet, a default value is set, otherwise the stored value is being returned The variable can now be used and updated (see customization below) The variable is being created/updated in the database for the next workflow run If the variable did not exist before it get’s created Fallback: If the table doesn’t exist, it automatically gets created Compatibility n8n Version 2.5.0 or above Cloud or Self-Hosted Note: Please make sure to use this feature with care when storing passwords or access tokens since those can be exposed via the n8n API when querying past executions with their data or data tables directly. Those kind of data should preferably stored inside of credentials directly.

BeginnerManual Trigger

Test WAF security interactively with an AI agent and WAFtester MCP

What it does A conversational AI agent that connects to WAFtester via MCP (Model Context Protocol) for interactive Web Application Firewall security testing. Type natural language requests — the agent picks the right tools, runs the tests, and explains the results. About WAFtester WAFtester is an open-source CLI for testing Web Application Firewalls. It ships 27 MCP tools, 2,800+ attack payloads across 18 categories (SQLi, XSS, SSRF, SSTI, command injection, XXE, and more), detection signatures for 26 WAF vendors and 9 CDNs, and enterprise-grade assessment with F1/MCC scoring and letter grades (A+ through F). GitHub: github.com/waftester/waftester Docs: Installation | Examples | Commands Who it's for Security engineers** running ad-hoc WAF assessments Penetration testers** who want AI-assisted reconnaissance and bypass discovery DevSecOps teams** validating WAF coverage before and after deployments API security teams** testing OpenAPI/Swagger specs against WAF rules How it works The workflow has four nodes: Chat Trigger — Opens an n8n chat interface where you type requests in plain English AI Agent — Receives your message, reasons about which tools to call, and orchestrates the testing workflow OpenAI Chat Model — Provides the LLM reasoning layer (GPT-4o recommended; swappable for Anthropic, Ollama, etc.) WAFtester MCP — Connects to the WAFtester server via SSE and exposes all 27 tools to the agent The agent follows a standard WAF testing workflow: detect_waf — Fingerprint the WAF vendor and CDN protecting the target discover — Map the attack surface (endpoints, parameters, technologies) from robots.txt, sitemaps, JavaScript, and Wayback Machine learn — Generate a prioritized test plan based on discovery results scan — Fire 2,800+ attack payloads and measure detection vs. bypass rates bypass — Systematic mutation matrix testing to find WAF evasion techniques assess — Generate a formal security grade with F1, precision, MCC, and false positive rate Long-running operations (scan, assess, bypass, discover, discover_bypasses, event_crawl, scan_spec) run asynchronously — the agent polls for results automatically. Key capabilities | Capability | Details | |---|---| | WAF detection | Fingerprint 26 WAF vendors and 9 CDNs from response headers, cookies, and error pages | | Payload scanning | 2,800+ payloads across 18 attack categories | | Bypass discovery | Mutation matrix with 40+ tamper techniques to find WAF evasions | | Enterprise assessment | F1 score, precision, MCC, false positive rate, and A+ through F grading | | API spec testing | Validate, plan, and scan OpenAPI/Swagger/Postman specs | | Headless crawling | Click-driven DOM crawling via headless browser for JS-rendered endpoints | | Knowledge resources | 12 built-in resources covering WAF signatures, evasion techniques, OWASP mappings, and config defaults | Example prompts "What WAF is protecting https://example.com?" "Scan https://example.com for SQLi and XSS" "Find WAF bypasses for https://example.com" "Run a full security assessment of https://example.com" "Validate my API spec at https://example.com/openapi.json" "Discover the attack surface of https://example.com" How to set up Start WAFtester MCP server: docker run -p 8080:8080 ghcr.io/waftester/waftester:latest mcp --http :8080 Add OpenAI credentials in n8n: Settings → Credentials → New → OpenAI API Select the credential in the OpenAI Chat Model node Activate the workflow and open the chat interface Alternatively, use the included docker-compose.yml to run both n8n and WAFtester together with docker compose up -d. Requirements | Requirement | Details | |---|---| | WAFtester MCP server | Docker image (ghcr.io/waftester/waftester:latest) or binary install for macOS, Linux, Windows | | LLM API key | OpenAI (default), or swap the model node for Anthropic, Ollama, Azure OpenAI, or any LangChain-compatible provider | | Authorization | Only test targets you have explicit written permission to test | Links WAFtester website GitHub repository Installation guide Full examples Docker Hub

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