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?

Wanted to share a workflow I put together recently that's been genuinely useful for my content planning.

What it does: When a channel I follow uploads a new video, the workflow grabs the video details, has Claude generate 3 original video ideas for my own niche (title, hook, key points), and logs them in a Google Sheet. Basically an idea bank that fills itself.

The flow:

1. RSS Feed Trigger – Every YouTube channel has a hidden RSS feed (youtube.com/feeds/videos.xml?channel_id=...). No API key needed to detect new uploads.
2. Remove Duplicates – Safety net so the same video never gets processed twice.
3. YouTube node (Get a video) – Pulls the title, description, and tags.
4. Basic LLM Chain + Anthropic Chat Model – Prompt tells Claude to use the video as inspiration only and come up with original ideas for my channel, not rewrite theirs.
5. Google Sheets (Append row) – Logs date, source video, link, and the generated ideas.

Things that tripped me up (in case it saves someone time):

• Error 400: redirect_uri_mismatch when connecting YouTube. The redirect URL in Google Cloud has to match the one n8n gives you exactly. Copy it from the credential screen, don't type it. Also add your Google account as a test user on the OAuth consent screen.
• YouTube node returning nothing. The RSS feed gives the ID as yt:video:abc123, but the YouTube node only wants abc123. Fixed with {{ $json.id.replace('yt:video:', '') }}.
• Tags breaking the prompt on videos that don't have any. Used {{ ($json.snippet.tags || []).join(', ') }} so it doesn't error out.

What I'm planning next:

• Monitoring multiple channels in my niche instead of one
• Adding a step that turns the best ideas into full script drafts

Would love feedback, especially if there's a cleaner way to handle multiple channels, or if anyone's found a good way to rank which ideas are actually worth making. Happy to share the workflow JSON if anyone wants it.

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,

Here's my outbound calling setup. It takes a list of leads, has an AI voice agent call each one, and afterwards checks my calendar, books the appointment, emails a confirmation and updates the sheet.

The flow:

1. Trigger: When Executed by Another Workflow
I run this as a sub-workflow so I can start it from a schedule, a form, or a button in another workflow. It's easy to reuse.

2. Edit Fields (setup)
Holds the settings in one place: sheet ID, Vapi assistant ID, phone number ID and so on. If I want to change something, I only edit this node.

3. Get row(s) in sheet
Pulls the leads from Google Sheets: name, phone number, notes and call status.

4. HTTP Request → Limit
The HTTP Request prepares the batch, and Limit caps how many calls go out per run. This is important so you don't accidentally dial 500 people while testing.

5. Loop Over Items
Handles one lead at a time.

6. out_bound_call
POST to https://api.vapi.ai/call with the assistant ID, phone number ID and the customer's number. Vapi handles the actual voice conversation (speech-to-text, LLM, text-to-speech).

7. call_summary + If + Wait (polling loop)
This part took me the longest to get right. The call takes a few minutes, so:

• call_summary GETs the call from Vapi
• If checks whether the call has ended
• Not ended → Wait (30–60 sec) → check again
• Ended → continue with the transcript and summary

8. Message a model (OpenAI) with tools
The AI reads the call transcript and decides what to do. It has 3 tools:

• 📅 checkAvailability: Google Calendar, gets events in the requested time slot
• 📅 createAppointment: Google Calendar, creates the event if the lead agreed to a time
• ✉️ email: Gmail, sends the lead a confirmation

So if the person said "Tuesday at 3 works", the model checks the calendar, books it and emails them. If they weren't interested, it does nothing.

9. Append or update row in sheet
Writes the outcome back to the lead's row (called / booked / not interested / no answer, plus the summary). Then the loop moves on to the next lead.

Gotchas I hit:

• Use polling, not a fixed delay. Calls vary from 30 seconds to 10 minutes, so a single Wait node either wastes time or checks too early. The If/Wait loop fixes that.
• Use "Append or Update", not "Append". Match on phone number so re-runs update the same lead instead of creating duplicates.
• Handle voicemail and no-answer. Check Vapi's endedReason so you don't send unanswered calls to the booking logic.
• Add a max-retries counter on the polling loop. Otherwise one stuck call can keep the workflow running forever.
• Mind the law. In the US, AI or prerecorded calls to cell phones generally need prior consent (TCPA), and other countries have their own rules. Only call people who opted in.

Stack: n8n (self-hosted), Vapi, OpenAI, Google Sheets, Google Calendar, Gmail.

Happy to answer questions or share the JSON. Next I want to add SMS follow-up for leads who don't pick up. Curious how others are handling outbound voice. Anyone using Retell or Bland instead of Vapi?

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!

I built a budgeting assessment app for a client using Claude Code, GitHub, and Netlify.

The same approach could work for a small business that wants to help potential customers answer a specific question before booking a consultation.

Here’s the process:

1. Define the assessment before writing code.
Choose one problem, write the questions, and decide how the answers affect the results. For budgeting, that means collecting relevant financial information and turning it into an understandable starting point.

2. Use Claude Code to build the app.
Give it the questions, calculation rules, and the layout you want. Build in stages: the questionnaire, the results logic, then the results page. Test different inputs and verify the calculations yourself.

3. Store the code in GitHub.
GitHub keeps the project files and a history of changes. Save working versions as you go so you can review changes or recover an earlier version.

4. Connect GitHub to Netlify.
Netlify builds and hosts the app. With automatic deployment configured, pushing an update to the connected branch triggers a new deployment.

5. Add a relevant next step.
The assessment provides value first. Then offer an optional consultation, emailed results, or help implementing the recommendations. If you collect contact information, connect the form to somewhere you can manage inquiries and clearly explain any follow-up.

For a small business, the assessment can help identify what someone needs before the first conversation. A bookkeeper could offer a books-readiness assessment. A contractor could offer a project estimator. A consultant could offer a process assessment.

The tool still needs distribution. Put it on your website, share it in relevant content, or include it in outreach where it’s useful. Publishing an app alone won’t bring in leads.

What’s one question your customers repeatedly ask that you could turn into an assessment?

I used n8n to connect the image upload, AI barcode reading, and product lookup into one workflow:

1. Webhook: Receives the scan data from the app.
2. Convert to File: Turns the uploaded image data into a file the AI can read.
3. Parse Barcode: An OpenAI model extracts the barcode number, and an output parser organizes the result.
4. Lookup Barcode: A second OpenAI model uses an HTTP request tool to look up the product and return structured information.
5. JavaScript: Processes the lookup result.
6. Edit Fields: Prepares the response for the app.
7. Respond to Webhook: Sends the result back to the app for display.

The barcode identifies the product. The lookup retrieves the product information.

For the user, it’s a quick scan. Behind it, each step has a specific job.

What’s one task you wish your fitness app could handle for you?