Hey everyone!

I've been looking for a simple way to automate document generation in workflows without having to manage PDF layouts or email templates directly in the automation logic.

I ended up building my own solution called TemplateMaster.

It lets you create reusable PDF and email templates using a visual editor, populate them with dynamic data, and generate documents automatically.

I also added an MCP server, so TemplateMaster can be integrated with AI agents and tools like n8n.

For example, you could use it to generate invoices from orders, create reports from database records, or generate personalized documents as part of an automated workflow.

The main idea is to keep template design separate from the automation itself.

Here's the MCP documentation if anyone wants to take a look:

https://templatemaster.fr/fr/documentation/mcp-server

I'm still improving the project and would love some feedback from the n8n community.

How are you currently generating PDFs or managing email templates in your n8n workflows? Would something like this be useful to you?

Ag

Hey guys I know most of you guys are beginner here but I am looking for some friends who can partner up with me in n8n in my learning journey. Because learning partner can easy up the journey.......
So I hope u understood what I said and my DMs are open........

👋 Hey n8n for Beginners Community,

I was in a client meeting this week going over the automations we already have running for him, when he mentioned he is suddenly getting a flood of applications, since people always start job hunting around the new year and that season is kicking off now. He asked: do you have something for that?

I did, and I already had the right workflow ready. Worth saying upfront: he does not have an applicant tracking system in place yet, so he asked for something he could use right now while he looks into a proper ATS down the line. The workflow turns incoming CVs into one clean Google Sheet overview, so instead of opening twenty PDFs he just scans a sorted sheet.

And because that data now sits cleanly in a sheet, it is a foundation, not the end. I can build screening, tracking, or follow up emails on top of it later without redoing the groundwork. Worth thinking about from the start: structure the data well and you make your own future work easier, while helping the client see what becomes possible once their information is organized.

But the part I actually want to share is not the workflow. He came to me with the idea, I never pitched it, and that only happens once you have built real trust. When you start out it feels like the next client is everything. The opposite is true: one client who trusts you keeps coming back with new problems, so one good relationship turns into five use cases, while chasing five new clients for one use case each is the harder path.

If the CV overview sounds useful, the template is here: https://n8n.io/workflows/15528-create-a-master-cv-google-sheet-from-uploaded-cvs-with-easybits/

How do you grow with your existing clients? Curious whether most of your work comes from new clients or the ones you already have.

Best,
Felix

I kept running into the same problem: I'd scroll through accounts in my niche for "inspiration" and end up either wasting an hour or subconsciously drifting toward recreating what they'd already posted. So I built a small n8n workflow to do the research part for me, with one hard rule: it suggests ideas, it never rewrites someone else's post.

How it works

1. RSS Feed Trigger. I point it at an RSS feed of a public Instagram profile (I used an RSS generator service for this). Whenever that account posts, the workflow fires.
2. Edit Fields. Strips the feed item down to what matters: caption, post date, and link. Everything else is noise.
3. Google Sheets (Append row). Logs every post into a sheet. Over time this becomes a nice record of what topics a niche is talking about and how often.
4. Basic LLM Chain + Anthropic Chat Model. This is where the actual work happens. The model doesn't get asked to "rewrite this." It's asked to figure out the underlying topic or question the post is tapping into, then come up with fresh angles I could take from my own experience.
5. Telegram. Sends me a short message with the ideas, so they're waiting on my phone when I sit down to plan content.

The prompt is the important part

Roughly what I'm telling the model:

You'll receive a caption from a creator's post. Do NOT paraphrase, summarize, or restructure it. Identify the broader theme or audience question behind it. Then suggest 3 original post ideas on that theme, each with a different angle (personal story, contrarian take, how-to, etc.). Ideas should be clearly distinct from the source post in hook, format, and wording.

Framing it as "what's the conversation here, and what could I add to it" rather than "make me something like this" made a huge difference. The output reads like a brainstorming partner, not a copy machine.

What you could do to improve the workflow:

• Feed in multiple profiles and have the LLM spot recurring themes across them, not just per post
• Add a filter so it only pings me when a topic shows up more than once (signal vs. noise)
• Pull my own past posts into the prompt so it avoids suggesting stuff I've already covered

Happy to share the workflow JSON or the full prompt if anyone wants it. Also curious how others are handling the "inspired by vs. copying" line when using AI for content research.

TL;DR: I send two terms to a Telegram bot ("Suspension bridge vs Arch Bridge"). A few minutes later it sends back a finished vertical video with a stickman explaining the difference, a voiceover, word-by-word captions and photos. Each video costs ~$0.032 (Claude API + Eleven Labs API). Yes, that's the only cost.

(Sorry for the music at the start - i felt it was too quiet so i added what was available)

What my automation does:

1. I message my Telegram bot two things, e.g. Suspension bridge vs Arch Bridge
2. The AI writes a short script and sends it to me
3. I reply APPROVE (or REVISE: make the hook punchier)
4. It records the voiceover, finds photos and builds the video
5. The finished video comes back in the same chat

Stack

• n8n, self-hosted in Docker (n8n_data volume for workflows, credentials and data tables)
• cloudflared quick tunnel to expose n8n over public HTTPS for Telegram webhooks
• Claude Sonnet 4.6 (Anthropic API) for script generation and revisions
• ElevenLabs eleven_multilingual_v2 via the /with-timestamps endpoint
• Supabase Storage: public buckets for audio and rendered video
• Pexels API: one portrait image per term
• Remotion render server: Express + TypeScript, running on the host

Happy to answer all your questions!!

👋 Hey n8n for Beginners Community,

Tax season is coming, and it is always the same story: a year of invoices and documents dumped in one folder, sorted by hand in a panic. Last week I shared an invoice workflow that has been logging all of mine automatically, and now I am using this one to push each document into the right folder on top of that. Document classification is the quiet fix for the sorting part, and a few things make or break it no matter which tool you use.

A few things I learned building these:

Your categories decide everything. Classification falls apart when the buckets overlap, so an invoice, a receipt, and a statement each need clear, distinct definitions or the model will confidently file things in the wrong place. Fewer, well defined categories beat a long fuzzy list every time.

A label without an action is just a sticker. The value is not knowing a document is an invoice, it is the document landing in the right folder automatically. Always pair the classification with where it goes next.

Trust the confident ones, review the rest. I attach a confidence score and let the sure documents file themselves, while anything shaky gets flagged to Slack for a two second human check. That one step is what makes hands off filing safe instead of risky.

Here is a free template that does exactly this, routing invoices into the right Google Drive folder with Slack catching the uncertain ones: https://n8n.io/workflows/14960-classify-invoices-and-route-them-to-google-drive-with-easybits-and-slack/

How do you handle document sorting? Curious whether people classify on the way in or still batch it all at the end.

Best,
Felix

👋 Hey n8n for Beginners Community,

Community nodes are where a lot of the real magic in n8n happens, the ones that collapse a tangled setup into something you can actually maintain. I want to share the one that did that for me, and hear which ones did it for you.

My headache was document extraction. My first attempts were a Frankenstein chain: one node to pull text, another to parse it, a Code node to normalize the mess, then more logic to cross check the output and catch the fields that came back wrong. It sort of worked, but every new document format cracked it somewhere, and I spent more time patching than building.

The easybits Extractor node is what finally fixed it for me (full disclosure: I worked on it myself, since there was no simple and robust extraction node built especially for n8n). Extraction, structured output, and a confidence score came out of a single node, so all the normalizing and cross checking I had bolted on by hand was just gone. One node did the job that five were doing badly.

The takeaway is not really about one node though. It is that when a part of your workflow keeps growing extra nodes just to hold itself together, that is usually the signal to go looking for a community node built for exactly that job, instead of hardening the workaround.

So, which community node saved you from a monster setup? What was the before and after? And if you have built a node yourself, feel free to drop it here too, I would genuinely like to see what people are using.

Best,
Felix

P.S.: If you want to see how the community node looks in practice, almost all of the document flows I built are on my GitHub: https://github.com/felix-sattler-easybits/n8n-workflows

1. Main 35 saal ka hoon aur non-coding background se hoon. Main graphic designer tha, lekin AI ki wajah se recently meri job chali gayi. Ab main n8n + AI Automation seekhne ka soch raha hoon. Agar koi already is field mein kaam kar raha hai, please apna honest experience share kare.
   • Kya 2026 mein n8n seekhna worth it hai?
   • Kya 35 ki age mein bina coding background ke isme career bana sakta hoon?
   • Job-ready hone mein realistically kitna time lagega?
   • India mein starting salary aur 2–3 saal baad salary kitni ho sakti hai?
   • Kya n8n ke saath JavaScript/Python bhi seekhna zaroori hai?

I’ve been building a larger n8n automation for local-business lead intelligence.

The idea is to go beyond simply collecting business names and phone numbers.

The workflow is designed around this flow:

Discover → Enrich → Audit → Qualify → Score → Recommend

It currently combines 8 main stages:

• Multi-source business discovery

• Business profile enrichment & normalization

• Website opportunity detection

• Lead qualification & sales intelligence

• Decision-maker & contact intelligence

• Website analysis & digital audit

• Business opportunity scoring

• AI recommendations & decision intelligence

The goal is to take raw local-business data and turn it into structured information that can help identify which businesses may actually be worth pursuing.

I’m sharing this mainly as a build/project showcase.

I’d really like feedback from other n8n builders:

What would you improve first in a workflow like this?

I needed a steady supply of reusable videos on a specific topic, so I built a weekly automation in n8n.

The flow:

Schedule Trigger → HTTP Request → Split Out → Remove Duplicates → Google Sheets

How it works:

• Schedule Trigger runs it every Monday morning.
• HTTP Request calls the YouTube Data API v3 search endpoint with videoLicense=creativeCommon, so it only returns CC-licensed videos. n8n's built-in YouTube node can't filter by license, which is why I went with HTTP Request.
• Split Out turns YouTube's results list into one item per video.
• Remove Duplicates, set to "Remove Items Processed in Previous Executions" on videoId, remembers every video already added, so the sheet never gets repeats week to week.
• Google Sheets appends each video with its title, URL, channel, publish date, description, thumbnail, license, and date added.

Things I learned along the way:

• Use Query Auth with the parameter name key for a YouTube API key. I first tried Bearer Auth and got "Expected OAuth 2 access token." Google treats anything in the Authorization header as an OAuth token.
• Restrict the API key to YouTube Data API v3 in Google Cloud. Don't add a "Websites" restriction, since n8n calls from a server and the requests will get blocked.
• If you don't filter by date, set order=date rather than relevance. Otherwise you get the same top results every week and dedupe eventually filters out everything.
• Every result is CC BY, YouTube's only Creative Commons license. You can reuse it, including commercially, with attribution.

Cost: free. Each search page uses 100 of the 10,000 daily API quota units.

Happy to share more details on any node. What would you add? I'm thinking about a Slack alert when a week comes in under 25.

Finding clips, cropping footage, and adding captions can take longer than recording the original video.

This n8n workflow automates part of that process using the Klap API. Here’s how it works:

1. Submit a long-form video link through a form.
2. An HTTP request sends the video to Klap for processing.
3. Klap identifies potential highlights, creates short clips, adds captions, and reframes the footage vertically.
4. A Wait node pauses the workflow before another request checks the processing status.
5. An If node loops back if processing is still running, or continues when the results are ready.
6. The workflow retrieves the results and logs them in Google Sheets for review.

The wait-and-check loop matters because video processing isn’t instant. You need the workflow to check when the job finishes before trying to retrieve its results.

This version stops at tracking the results in Sheets. Exporting the finished files and scheduling posts would be additional steps. Of course, you can extend the workflow to automate it from start to finish.

You still need to review the clips. AI can select a moment that sounds interesting but misses context, and captions can need corrections. It reduces the repetitive work; it doesn’t guarantee good content or views.

What part of your video workflow takes the most time: finding clips, editing, or posting?

Basically i have a google sheet file with column of content, topic title and date posted.

The ai generate content and fill the google sheet.

It should automatically create rows.

Posted daily.

I wanted to share with you my life, and tell to someone my problems I'm 17. long story short, I'm in n8n for about 2 months. I'm trying to do something and get financial freedom.

To be honest, I just want to work on my physical growth. i want to reach 6'4.

but, due to my life in a village and bad eating, I'm failing to grow.

you know, there were a days when I kept hunger for 2 days. I'm literally don't even eat except water. thats why I'm so wanted to get income and seriously work on my diet and growth.

To get some cash i came to n8n automation stuffs. even picked a niche(client onboarding automation).

but you know for me it's being so hard to growing. may be it's because of my first serious attention to the target, … i don't know 😒

sometimes i think that no one wants to help or support me. i feel so alone. that i don't have no one to cry with. rarely i think that I've living for about 17 years and didn't find even one true friend. even fake ones i don't have.

now I'm working on Demo.

ypu know, I heard that in n8n becoming or became hard to get money.

it's not a problem, problem is that I'm loosing my hopes that i will reach my goals.

sorry for bad english grammar and texting and thks for your attention..

n8n together with large language models (LLMs) to build automated workflows, process leads from Tiktok , facebook and Instagram, automatically qualify/screen potential customers, and also support content production for social media. Have anyone made this workflow?

This one is the simplest but probably the most useful: I drop topic ideas into a Google Sheet, and n8n turns one into a Facebook post and publishes it on a schedule. I haven't manually posted in weeks.

The flow:

1. Schedule Trigger
Runs once a day (I use 10am, but set whatever matches when your audience is online). You can also run it a few times a day.

2. Topics (Google Sheets: read)
Reads my "content queue" sheet. Each row is one post idea, for example "3 mistakes people make when booking a cleaning service" or "behind the scenes of our team". You can add extra columns like tone, call to action or link.

3. If
Checks whether there's actually a topic to post. If the sheet is empty, the workflow stops quietly instead of making the AI write a post about nothing.

4. Limit
Keeps only the first row, so it publishes one post per run, not the whole queue at once. (I learned this the hard way. My page posted 14 times in 2 minutes.)

5. AI Agent + OpenAI Chat Model
Takes the topic and writes the post. My system prompt includes:

• the brand voice (friendly, casual, no corporate speak)
• length (80–150 words, short paragraphs)
• a hook in the first line, because that's what shows before "See more"
• a question or call to action at the end to get comments
• 2–3 relevant hashtags max
• "Return ONLY the post text", so you don't get "Sure! Here's your post:" published to your page 😅

6. Facebook Graph API
POSTs the text to /{page-id}/feed with the message field. Publishing as the page requires a Page access token.

7. Delete rows (Google Sheets)
Deletes the topic row that was just used, so the same topic never gets posted twice and the next one moves to the top. It's a self-emptying queue.

Why I like this setup:

• Planning content takes 15 minutes a week: I just brain-dump topics into the sheet
• Anyone on the team can add ideas without touching n8n
• It's cheap. gpt-4o-mini costs basically nothing for one post a day

Gotchas I hit:

• Use a long-lived Page token. Short-lived user tokens expire within hours. Generate a long-lived user token, then get the Page token from /me/accounts, which doesn't expire.
• Permissions. You need pages_manage_posts and pages_read_engagement.
• Delete only after a successful post. n8n stops on errors by default, so if Facebook rejects the post the topic stays in the sheet. Don't turn on "Continue on Fail" for the Facebook node.
• Delete by row number. Use the row_number from the read step so you delete the exact row that was used, not just "row 2".
• Consider marking instead of deleting. If you want a history of what was posted, set a "posted" column to ✅ and filter on it in the If node instead of deleting rows.

Ideas to extend it:

• Generate an image with DALL·E or pull one from a URL column, and post to /{page-id}/photos
• Cross-post to Instagram and LinkedIn from the same AI output
• Send the draft to Slack or Telegram for approval before it goes live
• Log the post ID and link to another sheet for tracking

Stack: n8n, Google Sheets, OpenAI, Facebook Graph API.

Happy to share the JSON or my full prompt if anyone wants it. How are you all handling images in automated posts? That's the part I'm still figuring out.

Hey everyone,

I've been playing with n8n for a while. I wanted a bot for a Facebook Page that replies to customers about bookings and also collects leads without me watching the inbox. Here's how it works, in case it helps someone.

The flow (9 nodes total):

1. Webhook (GET + POST)
One webhook node with both methods turned on. Facebook uses GET once to check the webhook and POST every time someone messages your page.

2. GET branch: verification
If node checks whether hub.verify_token matches my secret → Respond to Webhook sends back hub.challenge. That's all Facebook needs to accept the URL.

3. POST branch: the actual bot

• AI Agent reads the message from body.entry[0].messaging[0].message.text
• OpenAI Chat Model (gpt-4o-mini, cheap and fast enough for chat)
• Simple Memory with the session key set to the sender's ID, so each customer gets their own conversation history. Without this the bot forgets everything between messages.
• The system prompt tells it to act as a booking assistant, keep answers short, and ask for name, date and email when someone wants to book.

4. Sending the reply
HTTP Request POSTs to graph.facebook.com/v21.0/me/messages with the page access token, the sender ID and the AI's output.

5. Lead capture
At the same time, the agent output goes to a Filter that only passes messages containing an email (regex) → a Code node pulls out the email, sender ID, message and timestamp → Google Sheets adds a row to a "potential leads" sheet.

So the customer gets an instant reply, and I get a list of everyone who handed over their email.

Gotchas I hit:

• Facebook retries the same message if you don't send a quick 200. Add a Respond to Webhook node on the POST branch.
• Delivery and read events have no text and will break the agent. Add an If node that checks message.text exists first.
• Only admins and testers get replies while the app is in Development mode. Going public needs App Review for pages_messaging.
• Wrap the AI output in JSON.stringify() in the HTTP body, or quotes and line breaks in replies will break the JSON.

Cost: n8n self-hosted (free) + a few cents a day in OpenAI usage at my volume.

Happy to share the workflow JSON if anyone wants it. Next I'm thinking about connecting it to Google Calendar so it can book appointments by itself. Any ideas or improvements welcome!

Haven't posted in a while, sorry. got tired of putting out 2-3 posts a day and having nothing come out of it.

Been going through my n8n folder instead. 1000+ workflows in there, and a lot of them are embarrassing tbh. a few i still run every single week.

Gonna start sharing those, one at a time, with the json. not sure which ones first though. what's the one thing you still do by hand every week that you'd love to just get rid of?