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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

by Influencers Club

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

Run weekly WAF security audits with WAFtester and Slack alerts

What it does Automated weekly WAF security assessments with Slack reporting. Detects your WAF vendor, runs a security assessment, grades your protection, and alerts your team when the grade drops below threshold. 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 teams needing continuous WAF monitoring DevOps engineers tracking WAF configuration drift Compliance teams requiring regular security assessments How it works The workflow has seven nodes: Weekly Schedule — Triggers every Monday at 3 AM (configurable) Detect WAF — Calls WAFtester's detect_waf tool to fingerprint the WAF vendor and CDN Start Assessment — Launches an async assess task testing SQLi, XSS, traversal, cmdi, and SSRF Wait — Pauses to let the assessment run Poll Results — Calls get_task_status to retrieve completed results Check Results — Routes based on the WAF grade (pass if "A" or better, fail otherwise) Slack (Pass/Fail) — Posts a summary to your Slack channel with grade, detection rate, and bypass count How to set up Start WAFtester MCP server: docker run -p 8080:8080 ghcr.io/waftester/waftester:latest mcp --http :8080 Set environment variables: WAF_TARGET_URL (required), WAFTESTER_MCP_URL, SLACK_CHANNEL Add Slack OAuth2 credentials and select them in both Slack nodes Activate the workflow Alternatively, use the included docker-compose.yml to run both n8n and WAFtester together. Requirements | Requirement | Details | |---|---| | WAFtester MCP server | Docker image (ghcr.io/waftester/waftester:latest) or binary install | | Slack | Workspace with OAuth2 bot credentials | | Authorization | Only test targets you have explicit written permission to test | How to customize Adjust schedule in the Weekly Schedule node Change grade threshold in the Check Results node Add attack categories in Start Assessment's categories array Swap Slack for email, Teams, or any n8n notification node Links WAFtester website GitHub repository Installation guide Full examples Docker Hub

Gate deployments on WAF scan results with WAFtester

What it does A CI/CD quality gate that blocks deployments when WAF protection is insufficient. Your pipeline sends a webhook with the target URL, the workflow runs WAFtester scans, and returns a pass/fail HTTP response the pipeline can gate on. 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 DevOps teams** enforcing security gates in CI/CD Platform engineers** automating deployment approvals Security teams** requiring pre-deploy WAF validation How it works The workflow has seven nodes: Webhook — Receives a POST with {"target": "https://staging.example.com", "categories": ["sqli", "xss"]} Detect WAF — Calls WAFtester's detect_waf tool to fingerprint the WAF vendor Start Scan — Launches an async scan task with the requested attack categories Wait — Pauses to let the scan run Poll Results — Calls get_task_status to retrieve completed results Evaluate — Compares the detection rate against WAF_PASS_THRESHOLD Respond — Returns HTTP 200 (pass, deploy allowed) or HTTP 422 (fail, deploy blocked) with bypass details CI/CD integration example In your pipeline RESPONSE=$(curl -s -w "%{http_code}" -o body.json \ -X POST https://your-n8n/webhook/waf-gate \ -H "Content-Type: application/json" \ -d '{"target": "https://staging.example.com", "categories": ["sqli", "xss"]}') if [ "$RESPONSE" != "200" ]; then echo "WAF gate failed"; exit 1; fi

BeginnerManual Trigger

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