Custom Workflow Automation Scripts

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 just built a custom AI agent that automated my entire AWS deployment workflow in under 10 minutes. Not with weeks of coding. Not with complex infrastructure. Just by defining what I wanted my agent to do with Qodo's new Command tool. Here's how I turned 8 manual steps into 1 command 👇 The Problem I Solved: Every deployment meant manually running AWS CLI commands, checking configurations, invalidating caches, and hoping nothing broke. Time-consuming and error-prone. My Solution: Created a custom Qodo Command agent that handles everything with: qodo deploy-static What My AI Agent Now Does: → Verifies AWS CLI and credentials automatically → Creates/configures S3 buckets on the fly → Syncs all files from my local folder → Updates CloudFront distributions with OAC → Invalidates caches for updated files → Tests deployment with HTTP verification I didn't need to learn complex AI frameworks. I just told the agent what I wanted it to do, when to run, and what tools it could use. That's it. Your workflow, your rules, your agent. My Results: Deployment time: 10 hours → 10 minutes Manual steps: 8 → 1 Coffee breaks during deployment: ∞ → 0 Why I'm Excited About This: Every developer has workflows that waste hours each week. With Qodo Command, I can turn ANY repetitive task into an autonomous agent. 2025 isn't about waiting for AI to solve our problems. It's about building our own AI agents for our specific needs. Over to you: What workflow are you automating first? Try Qodo Command here: https://lnkd.in/dcWv2Fai

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?