Custom Workflow Automation Scripts

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

I just solved a workflow problem that was eating hours of my time every week - and I want to share how I did it. Like many content creators, I was manually converting my Beehive newsletter drafts into markdown for my website. Copy, paste, reformat, fix images, adjust embeds... you know the drill. It was tedious and error-prone. So I built a custom MCP (Model Context Protocol) server in Java that: • Connects directly to Beehive's API • Pulls draft content automatically • Converts HTML to my specific markdown format • Handles images, YouTube embeds, and Twitter posts • Creates files in the right directory structure The best part? I can just tell Claude: "Grab the latest draft and create the markdown file for my website" - and it handles everything. This isn't just another toy tutorial. It's a real solution to a real problem that saves me hours every week. The MCP server gives Claude the exact tools it needs to automate complex workflows that would be painful to script manually. I've even set up GitHub Actions to build native images for Mac, Windows, and Linux - so you don't need Java installed to use it. The source code is available on GitHub if you want to see how it works or build something similar for your own workflow. What manual tasks in your workflow could benefit from this kind of automation? Sometimes the best solutions come from scratching your own itch. Watch the full demo: https://lnkd.in/e-M2fMZy ##MCP #Java

I turned Claude into my personal workflow automation engine using nothing but slash commands and markdown. The gist: you design complex workflows as custom Claude Code commands that guide you through multi-step processes, pulling data from systems, updating others, and handling tasks that need human judgment - all without tab-switching into oblivion. Here’s how I’m building these: 1 - Sketch the workflow first I use Mermaid diagrams. Not just because I love diagrams, but because I can feed them directly to the agent to help it orchestrate better. Visual structure = better execution. 2 - Break big workflows into Lego blocks Learned this the hard way. Started with one massive workflow file. Total mess, impossible to test. Now I break things down. My ideation workflow? Actually three smaller workflows that call each other: Gather insights and analytics, then prompt for ideas based on real problems Deep dive on the promising ones Design quick tests to de-risk before building Way more flexible. Way less brittle. 3 - Keep steps dead simple Each step does ONE thing. When a step starts doing two things, split it. Makes debugging 10x easier when something inevitably breaks. 4 - Structure everything with markdown & XML Sounds nerdy, but it works. I use XML properties to annotate steps and shift the LLM's behavior for each step. For example, sometimes I want the LLM to act more like a facilitator when executing a step, prompting me for input and guiding me towards a better result. Other times, I just want it to do something like grab data from other systems. 5 - Let the LLM update its own workflows Meta, but practical. Since everything's in Mermaid and structured text, I can ask it to refine its own workflow based on what's working. Saves me tons of time. 6 - Version control everything Git isn't just for code. When you inevitably break a working workflow prompt at 4 pm on a Friday, you'll thank yourself for that commit history. The result? Over the past few weeks, we’ve run several ideation sessions and saved hours pulling data and creating tickets in Vistaly and GitHub. I also started sharing these commands with customers and started to see them run with them and make updates. So cool. Who else is building custom workflows like this? What's the most complex thing you've automated with your LLM/MCP? Drop a comment or DM me if you want to swap workflow files. Building a small library of these things.

Most people think AI automation is complex. It's actually just 7 building blocks. I've been using n8n for the past year to automate tasks, and these core nodes handle 95% of what you need. Here's the essential toolkit: 1️⃣ Code Node Run JavaScript or Python for custom logic. Example: Convert JSON data to XML before sending to legacy CRMs. 2️⃣ HTTP Request Connect to any API or web service. Example: Pull lead data from Apify, push qualified leads to Slack. 3️⃣ Edit Fields Clean and standardize your data. Example: Rename "first_name" to "nombre" for international teams. 4️⃣ IF Node Create conditional paths in your workflow. Example: Active leads → sales team, inactive leads → nurture campaign. 5️⃣ Switch Node Handle multiple conditions at once. Example: Route different API responses (200, 404, 500) to appropriate handlers. 6️⃣ Loop Over Items Process lists of data individually. Example: Send personalized emails to 100 prospects without overwhelming your server. 7️⃣ Error Handling (The unsung hero) Not technically a node, but critical for production workflows. Always have a Plan B when APIs fail. Why this matters for lead generation: Last week, I built a workflow that: - Build lead list for my clients - Enriches them with company data - Scores them with AI - Routes to the right sales rep - All in under 2 minutes per lead No complex coding. No expensive consultants. Just these 7 nodes connected properly. Once you understand these basics, you can automate almost any business process. n8n makes it visual. Drag, drop, connect, done. Over to you: Are you heavy n8n user or exploring if it's a right fit for you?

Remember the fragile, soul-crushing world of browser automation? Writing complex scripts with Selenium or Puppeteer, hunting for CSS selectors that break the moment a website changes a button color... It was a power reserved for specialized developers with a lot of patience. What if you could automate any browser workflow... just by talking to it? Not with code, but with plain English commands like: - "Click the 'See all people' button." - "Extract the name, headline, and profile link for everyone on this page." - "Type this personalized message into the text box." This isn't theory. I've been using a new workflow that turns natural language into browser actions using Airtop and n8n. To put it to the test, I gave it a complex, real-world task: find potential leads on LinkedIn, extract their data, filter the good from the bad, and draft personalized connection notes. The agent navigated the complex UI flawlessly. This isn't just about lead gen. This is a fundamental shift in how we interact with the web. It means: MARKETERS can automate competitive research without writing code. FOUNDERS can build custom scraping tools for market data in minutes. DEVELOPERS can build robust browser automations without the brittleness of CSS selectors. This democratizes a superpower that used to be locked away behind complex code. I've wrapped up this entire workflow into a free N8N template that you can adapt for any browser automation task. Comment BROWSE and I'll send it to you. You can watch the full, step-by-step build in my latest video: https://lnkd.in/ehFPfxCw

Part-3- Yesterday, I implemented a complete API automation workflow using Postman, Environment Variables, Request Chaining, and Collection Runner.(End-to-End CRUD Flow) Below is the structured flow I executed: 1. Environment Setup, Created a new environment: Test Environment 2. Added variable: base_url = [https://api.example.com. ](https://api.example.com) 3. Used {{base_url}} in all request URLs to avoid hardcoding. 4. Pinned the environment to the collection for consistent execution. [I have some 3-4 collections available in my workspace, so i pinned the env to this one] 2. Collection ApI requests Chaining Creation Process- 3. Created a new collection: I named it- UserCollectionManagementAPIs 4. Organised all User APIs (Login, Create, Update, Delete) under this collection. 5. Starred the collection so that it will come on top.. 3. Login Request – POST Authentication 4. Created POST request: {{base_url}}/login 5. Added: * Body payload (username & password) * Header: x-api-key 6. Executed request and validated: Post-response- Test Script: pm.test("Status code is 200", function () { pm.response.to.have.status(200); }); 2nd validation check- pm.test("Response time is less than 1000ms", function () { pm.expect(pm.response.responseTime).to.be.below(1000); }); 4. Extracted token dynamically from response and saved it as environment variable: let jsonData = pm.response.json(); pm.environment.set("token", jsonData.token); This allowed dynamic authentication for next requests. 4. Then, Create User – POST Request 5. Created POST request: {{base_url}}/users 6. Authorization: Bearer Token → {{token}} 7. Added JSON body for user creation. 8. Validated response: pm.test("User created successfully", function () { pm.response.to.have.status(200); }); 5. Extracted user ID and stored it: let jsonData = pm.response.json(); pm.environment.set("userid", jsonData.idd); This enabled dynamic chaining for update and delete operations. 5. Update User – PUT Request 6. Created PUT request: {{base_url}}/users/{{userid}} 7. Used Bearer Token authentication. 8. Added updated payload. 9. Added validation: pm.test("User updated successfully", function () { pm.response.to.have.status(200); }); 6. Delete User – DELETE Request 7. Created DELETE request: {{base_url}}/users/{{userid}} 8. Used Bearer Token authentication. 9. Validated response: pm.test("User deleted successfully", function () { pm.response.to.have.status(204); }); 7. Collection Runner Execution 8. Executed full collection via Collection Runner. 9. Configured: * Iterations: 1 * Delay: 0ms 10. Reviewed execution summary. 11. Exported results for reporting and sharing. Key Concepts Practiced: * Environment variable management * Base URL configuration * Dynamic token extraction * Response data parsing * CRUD operation chaining * Bearer token authentication * Collection level execution * Test Result summary #apitesting #postman #qualityassurance

🚀 Automate Azure VM Configuration with Custom Script Extensions Tired of repeating the same steps every time you deploy a VM in Azure? In this video, I walk through how to use #Azure Custom Script Extensions to automatically configure new and existing VMs—setting time zones, deploying apps with Chocolatey, and more using PowerShell. This tutorial is ideal for IT pros, system admins, and cloud engineers looking to save time and reduce manual tasks in Azure. 📺 Watch here: https://lnkd.in/gUQygJ6v #MVPBuzz #CustomScriptExtension #PowerShell #Microsoft #CloudAutomation #SysAdmin #ITPro #IaC