Quick overview This Slack ChatOps workflow uses AI agents and a pyATS MCP server to handle day-to-day data network housekeeping tasks: from on-demand checks and validations per device, to configuration change proposals that require human approval before commit. Read the docs here: https://cs.co/9004BEMGZm How it works A network engineer mentions the Slack bot in a channel or thread. The workflow acknowledges the request and captures the full message The request is sent to a Planning Agent connected to pyATS through read-only MCP tools. The agent determines whether the user wants operational data or a configuration change, gathers live device evidence if needed, and evaluates whether the request is safe. If the request is operational only, or if the proposed change is unsafe, the workflow replies in the same Slack thread with a clear technical summary, including the relevant device findings or the reason why no change will be made. If the request is a safe configuration change, the workflow creates one approval card per target device.
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SSlack chatbot powered by AI
This workflow offers an effective way to handle a chatbot's functionality, making use of multiple tools for information retrieval, conversation context storage, and message sending. It's a setup tailored for a Slack environment, aiming to offer an interactive, AI-driven chatbot experience. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Send and track Slack invoice requests with Gemini, Google Sheets and Gmail
Quick overview Youtube Video: https://youtu.be/42-oHh9w9Eo This workflow listens for Slack @mentions, uses Google Gemini to classify the request and extract invoice details, then reads and updates Google Sheets, generates invoice PDFs with PdfBro, sends invoices or reminders via Gmail, and posts confirmations or errors back to Slack. How it works Triggers when the workflow’s Slack app is mentioned in a specified Slack channel. Uses Google Gemini to classify the message intent (send invoice, remind invoice, or check status) and extract the recipient email and invoice due date. Routes the request based on the extracted intent and stops if the intent cannot be determined. For SEND_INVOICE, looks up the client in a Google Sheets “Client Bill” spreadsheet and posts a Slack error if no matching email is found. If a client is found, generates an invoice PDF with PdfBro, emails it to the client via Gmail, appends/updates the invoice record in a Google Sheets “Client Invoices” spreadsheet, and posts a Slack confirmation. For REMIND_INVOICE, finds an UNPAID invoice for the email in the “Client Invoices” Google Sheet, regenerates the invoice PDF with PdfBro, emails a reminder via Gmail, and p
Answer Slack knowledge base questions with Notion, Pinecone, and OpenAI
Quick overview This workflow syncs an internal Notion knowledge base to Pinecone on a daily schedule, then runs a Slack bot that answers questions in a specific channel using Pinecone retrieval and an OpenAI chat model, replying in a thread with citations back to the source Notion pages. How it works Runs on a daily schedule to sync your Notion knowledge base into a Pinecone vector index. Fetches all pages from a specified Notion database and then pulls each page’s full block content (including nested blocks). Combines each page’s blocks into a single document and attaches metadata such as the Notion title, URL, and last edited time. Splits each document into overlapping text chunks, generates OpenAI embeddings, and inserts them into Pinecone while clearing and rebuilding the target namespace. Triggers when a new message is posted in the configured Slack channel and ignores bot/system messages to prevent reply loops. Uses an OpenAI-powered agent that always queries Pinecone for relevant chunks (including source metadata) and generates an answer in the user’s language. Posts the response back to Slack as a threaded reply, including cited Notion sources. Setup Create a Notion interna