This article provides a complete, practical walkthrough of the Splitout Code Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automated Keyword Research with n8n: A Scalable Workflow for SEO and YouTube Optimization
   
   Meta Description:  
   Discover how a low-code n8n workflow integrates NocoDB, DataForSEO, and Social Flood to automate keyword research, filter keyword opportunities, and analyze search volume for both Google and YouTube.
   
   Keywords:  
   n8n keyword automation, SEO workflow n8n, YouTube keyword research, DataForSEO API, Social Flood API, NocoDB SEO, Google search volume, autocomplete keywords, scalable keyword research, content strategy automation
   
   Third-Party APIs Used:
   1. DataForSEO API – for search volume, CPC, competition, and keyword metrics.
   2. Social Flood (Custom Docker API) – for Google and YouTube autocomplete suggestions.
   3. NocoDB – as a backend database for storing keyword data.
   
   Article:
   
   ---
   
   Keyword Research Reimagined: Automating Google and YouTube Analysis with n8n
   
   In the fast-paced worlds of SEO and content creation, the right keywords can be the difference between viral success and digital obscurity. Whether you're producing YouTube content or optimizing for Google search, tapping into accurate keyword data is vital. However, manually researching keywords, filtering them for relevance, and collecting search volume data is cumbersome and unsustainable at scale.
   
   Enter automation engineer’s secret weapon: n8n. This article explores a robust and scalable no-code/low-code workflow built entirely within n8n that automates keyword sourcing, generation, and analysis using a suite of integrated APIs — notably DataForSEO, Social Flood, and NocoDB.
   
   Let’s break down how this workflow intelligently fetches, expands, filters, and stores high-volume keywords for both YouTube and Google search performance.
   
   ⚙️ Step 1: Triggers & Base Keyword Fetching
   
   This workflow is designed to be triggered either manually or on schedule (every 4 hours via cron expression). First, it calculates the previous day's date — helpful for periodic data updates.
   
   It then queries NocoDB to retrieve a list of base keywords from a centralized table. These “seed” keywords represent the starting point for discovery.
   
   🔍 Step 2: Second-Level Keyword Generation with Social Flood
   
   With base keywords in hand, the system sends them to a Docker-hosted instance of the Social Flood autocomplete API. This clever tool scrapes autocomplete suggestions from both Google and YouTube platforms — simulating what real users are searching for.
   
   Each keyword is sent with parameters like language, country, spell check, and search engine type (`ds=yt` for YouTube). This generates a rich list of related, trending keywords that would otherwise require manual research.
   
   🧹 Step 3: Keyword Cleaning and Optimization
   
   Raw autocomplete data can be noisy. Therefore, a custom JavaScript-coded node processes all fetched keywords:
   
   - Removes duplicates.
   - Filters out keywords longer than 80 characters.
   - Blocks phrases with more than 10 words.
   - Strips out special characters.
   - Cleans up whitespace.
   
   The system ensures that only relevant, polished keywords proceed to the next phase.
   
   📊 Step 4: Search Volume & CPC Metrics with DataForSEO
   
   Next, the lists of cleaned keywords are sent to DataForSEO’s real-time keyword metrics endpoint. The system batches API calls into chunks (up to 1000 keywords per request) for efficient querying.
   
   The response contains valuable data, such as:
   
   - Average monthly search volume
   - Cost-per-click (CPC)
   - Competition index
   - Top-of-page bid estimates
   
   Separate batch POST requests are made for both Google Search (with `search_partners: false`) and YouTube (by including `search_partners: true`), enabling a clear insight into keyword value per platform.
   
   🚦 Step 5: Filtering for Actionable Keywords
   
   Not all keywords are created equal. Using conditional filter nodes in n8n, the workflow weeds out keywords without proper value indicators. Only those with available CPC and monthly volume data make the cut — ensuring that your database maintains high-quality and market-relevant keyword opportunities.
   
   🧠 Step 6: Data Storage & Management with NocoDB
   
   This is where everything comes together.
   
   Each qualifying keyword gets added to two dedicated NocoDB tables:
   
   - Second Order Google Keywords
   - Second Order YouTube Keywords
   
   Before storing, the system first verifies if a keyword already exists in the database. If it does, it updates the record. Otherwise, it creates a new entry.
   
   But the real magic is in historical tracking. For every keyword, the workflow also stores monthly search data, mapping keyword ID, year, month, and volume. This allows for long-term trend analysis and strategic planning.
   
   🛠️ Infrastructure Requirements
   
   To replicate or modify this workflow, you’ll need the following components:
   
   - An n8n instance (self-hosted or cloud)
   - NocoDB (for storing all keyword and analytics data)
   - DataForSEO account (credits required for search volume API)
   - Social Flood API (Docker image available via GitHub)
   
   These tools, together with n8n, turn traditional keyword research into a self-updating, hands-free operation.
   
   📈 Why It Matters
   
   This workflow unlocks several advantages for digital marketers, SEO specialists, and YouTube creators:
   
   - Save hours of manual keyword research & data entry
   - Ensure consistent and up-to-date keyword strategies
   - Identify content opportunities before trends peak
   - Integrate with visual dashboards (from NocoDB or external BI tools)
   
   Whether you're building content calendars, running paid campaigns, or optimizing for organic growth, understanding keyword trends deeply and dynamically is invaluable.
   
   🚀 Final Thoughts
   
   Automation is no longer a luxury — it's a necessity in the data-driven content landscape. By leveraging n8n and integrating it with services like DataForSEO, NocoDB, and Social Flood, you're setting up a battle-tested keyword machine that works 24/7, boosting your efforts without burning your time.
   
   Ready to future-proof your SEO and YouTube keyword strategy? Let your automation do the heavy lifting.
   
   ---
   
   Let me know if you'd like a visual flowchart or tutorial-style version next.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.