Every time I wanted to find what was trending on Instagram, I’d end up opening the same accounts, scrolling through Reels, checking views, saving videos, reading captions… and then trying to remember why I saved half of them.

So I built a Reels Trend Watcher in n8n.

Now the workflow can:

→ Pull a list of Instagram accounts from Notion
→ Find their recent Reels with Apify
→ Track views, likes, comments, saves and shares
→ Detect new vs existing Reels
→ Download new videos
→ Send the actual video to Gemini for analysis
→ Extract the hook + full transcription + category + format
→ Translate the content
→ Store everything back in Notion

So instead of just having:

“This Reel got 80K views.”

I can start getting:

“This Reel got 80K views + this hook + this format + this topic.”

That’s where it gets interesting.

Because once you have enough data, you can start asking better questions:

Which hooks keep showing up in high-performing Reels?
Which formats are actually working?
Which topics are suddenly gaining traction?
Are certain accounts consistently outperforming their baseline?

The scraping part was fairly straightforward.

The harder part, IMO, is figuring out what to do with all the data after you collect it.

Right now I’m using n8n + Apify + Gemini + Notion for the workflow.

Next step is turning this from a “Reels database” into an actual trend detection system that tells me what patterns are worth paying attention to.

If you were building this, what would you prioritize first:

1. Viral hook detection
2. Trend/topic detection
3. Competitor monitoring
4. Predicting which Reels might take off

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!!

I've been working on my SEO content pipeline, and one of the things I wanted to automate was long-tail keyword research.

Once you have a seed keyword, the next step is figuring out what people are actually searching for, finding related queries, and identifying keywords worth exploring further.

Doing this manually across multiple keywords can get repetitive pretty quickly. You end up switching between different tools, collecting suggestions, organizing them, and trying to keep everything in one place.

So I decided to build an n8n workflow that automates this part of the research process and stores the results in Airtable.

Starting with a seed keyword

The workflow starts with a seed keyword that I've already stored in Airtable.

For example:

>n8n self-hosted requirements

When I trigger the workflow, it retrieves the corresponding Airtable record and checks its long-tail research status.

If the keyword needs to be processed, the workflow marks it as In Progress and starts researching keyword variations.

I wanted to keep this connected to Airtable so I could track which keywords have been processed instead of managing everything manually.

Finding related keywords with YepAPI

I didn't want to rely on a single source of keyword suggestions.

So I'm using YepAPI to collect keyword data through multiple research paths.

The workflow makes separate API requests for:

• Related keywords — finds keywords related to the original seed keyword.
• Keyword ideas — discovers additional keyword variations to explore.
• Autocomplete suggestions — collects search suggestions that can reveal more specific queries people might use.

Each API response is processed separately. The workflow splits the returned keyword lists into individual items and prepares them for the next stage.

This gives me multiple sets of keyword suggestions from the same seed keyword rather than relying on one list.

Getting keyword data

The autocomplete branch has an additional step.

After collecting the suggestions, the workflow sends them through another YepAPI request to retrieve keyword data.

This helps me bring the discovered keywords and their available SEO metrics into the same research pipeline.

The goal isn't just to collect a list of phrases. I also want the data organized so I can evaluate the keywords later.

Bringing everything together

Once the research is complete, the workflow combines the results from the different branches.

It standardizes the output, splits the responses into individual keyword items where needed, and prepares the records for Airtable.

In one execution, the workflow produced 26 output items:

• 7 related keyword items
• 6 keyword idea items
• 13 autocomplete-derived keyword items

These are the output counts from that execution, not necessarily 26 unique keywords. Deduplication is something worth checking when combining results from multiple sources.

Storing everything in Airtable

I use Airtable to keep the research organized.

Each keyword gets its own record, associated with the original seed keyword.

The table includes fields such as:

• Keyword
• Competition
• Search volume
• Keyword difficulty
• CPC

I can then review the results in one place instead of going back through individual API responses.

Once the records have been created, the workflow updates the original seed keyword's research status.

This also gives me a starting point for tracking the progress of each keyword through the pipeline.

What's next?

This is one part of a larger SEO content pipeline I'm building.

I don't want to jump straight from a seed keyword to generating an article. I'd rather break the process into smaller steps, with each stage producing structured data that the next stage can use.

The direction I'm working toward is:

Competitor Research → Seed Keywords → Long-tail Keywords → Subtopic Research → Content Planning → Article Writing

The long-tail keyword workflow is the next piece of that process.

I'm building this step by step, with n8n handling the automation and Airtable keeping the research organized.

Do you use separate tools for keyword discovery and metrics, or have you automated this part of your SEO workflow as well?