AI topic monitor that posts RSS article summaries to Slack

What it is

This n8n workflow reads news feeds on its own and tells your team about the articles that matter. Every five minutes it checks a list of RSS feeds. A model decides whether each new article fits the topic you care about. The relevant ones are opened in full, summarised in a Slack-ready layout and posted to a channel. Every article it looks at is logged in a Google Sheet, so none is handled twice. Nobody types a thing. Eighteen nodes sit in it, beside thirteen sticky notes.

Who it suits

Teams that follow a field and do not want to read forty blogs a day, such as analysts, editors and product or research teams. It suits anyone who can name a topic in a sentence and keep a list of feeds. Skip it if you need a person to judge each article, because a model decides here, and a model will sometimes file a good article under the wrong heading. Skip it too if the sites you follow have no RSS feed. Pick your topic with care.

You need a Google Sheet with two tabs, an OpenAI key, a Slack workspace with a channel, and a few RSS feed addresses.

Where to use it

Any n8n that can reach Google, OpenAI, Slack and the sites you follow will run it. It runs by itself, so the workflow must be switched on, n8n must be kept running, and the machine that hosts it must stay awake and online around the clock. A cloud account suits it better than a laptop that sleeps. Plan for it.

• n8n Cloud: bring in the file, connect your accounts and switch it on
• Your own n8n server: the same file, kept running day and night
• A local install: it checks the feeds only while n8n is open
• A team channel: point Slack at a channel made for this

Setup

Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. Build the sheet, import the file, add three credentials, pick the sheet and the channel in the nodes that need them, and write your own topic into the classifier.

1. In n8n, create a workflow, open its menu, choose Import from File and pick workflow.json.
2. In Google Sheets, make a spreadsheet with a tab rss_feed (columns rss_feed_url and website) and a tab article_database (columns article_url, summarized, summary, website, fetched_at and publish_date). Put one feed address and its site name in each row of the first tab.
3. Add a Google Sheets OAuth2 credential to the four Google Sheets nodes, then pick your spreadsheet in each.
4. Add an OpenAI credential to OpenAI Chat Model and OpenAI Chat Model1.
5. Add a Slack OAuth2 credential to Slack1 and pick the channel.
6. Open Relevance Classification for Topic Monitoring and describe your own topic in its two category descriptions.
7. Save the workflow and switch it on.

How to use it

After that, leave it alone. Add a feed by adding a row to the rss_feed tab. Read the Slack channel for the summaries, and open the article_database tab when you want to see what was kept, what was skipped and when each article was checked. Expect a larger first run, since every article in a feed counts as new at the start.

Example

A product team follows twelve blogs from tools it competes with. It describes its topic as news about AI note-taking apps and leaves the rest as it is.

The first run reads every post in every feed and sorts them. It is the slowest run, because each article is judged on its own and each relevant one is fetched in full. Forty are marked relevant and appear in Slack as short summaries with a link. The rest are logged as not relevant. Nothing doubles. On the next morning, only the posts published overnight arrive. A rival’s launch post reaches the channel at the next check after the feed lists it, and nobody has to hunt for it.

For newcomers and experts

If you are new. n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. An RSS feed is a page that lists a site’s newest posts in a plain format that programs can read. A classifier is a model asked to file a text into one of a few boxes. A reader service turns a web page into clean text. That is all it does here.

For experts. New articles are found by comparing feed links with the log, and the model is called only for those. The log is the memory.

Good to know

The notes come from n8n’s source and from OpenAI, Slack and Google pages read on 12 November 2026, and nothing was run.

• Not run: no feed was fetched, no model was called, no Slack post was sent and no spreadsheet was touched
• Repairs: the model nodes named no model, so n8n’s own default gpt-5-mini would have run, which OpenAI shuts down on 11 December 2026. The notes on the canvas said GPT-4o-mini, which goes on 14 January 2027, with gpt-6-luna as its replacement. Both nodes now name gpt-6-luna, and neither calls a tool. The Sheets nodes moved from a service account to OAuth2, because Google no longer lets service accounts made after 15 April 2025 open My Drive files. A feed that fails to load leaves an error item with no link, so Code now drops items that have no link. The original author’s links and ids were removed, and the note about the schedule now says five minutes, as the node does
• Unsure articles: the classifier drops an article that fits neither box. It is not logged, so it is judged again at every run
• Bad replies: a model reply the classifier cannot read stops the run
• Dates: the publish date comes from pubDate, which n8n reads as an ISO, HTTP, RFC 2822 or SQL date. An item without it stops the run
• Slack: a new app cannot post in every public channel, so add it to the channel or give it chat:write.public. Slack suggests keeping a message under about 4,000 characters. Each message ends with a link to the workflow, because Include Link to Workflow is on
• Cost: the first run classifies the whole current list of each feed. gpt-6-luna costs USD 0.1 per million tokens in and USD 0.5 per million out on OpenAI’s page
• Scraping: the reader is called without a key, and a key only raises its rate limit. Check the rules of the sites you follow and of your country
• Your data: titles, snippets and article text go to OpenAI and to the reader service, and the summaries go to Slack and your sheet
• Credentials: the JSON holds none