This article provides a complete, practical walkthrough of the Stopanderror Code Import Triggered 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:
   Automating Prompt Personalization: Load and Populate AI Prompts from GitHub Using n8n
   
   Meta Description:
   Learn how to automate prompt loading and dynamic variable substitution using GitHub and n8n for seamless AI workflows. Ideal for SEO, content generation, and customized outputs.
   
   Keywords:
   n8n, GitHub API, prompt engineering, automated workflows, AI prompts, dynamic variable replacement, low-code automation, Ollama, LangChain, AI agents, SEO prompt automation
   
   List of Third-Party APIs Used:
   
   - GitHub API (via n8n GitHub node)
   - LangChain AI Agent (via @n8n/n8n-nodes-langchain.agent)
   - Ollama (via @n8n/n8n-nodes-langchain.lmChatOllama)
   
   Article:
   
   Automating Customized Prompt Workflows Using GitHub and n8n
   
   In the age of AI-generated content, prompts become the cornerstone of creative and powerful outputs. But what if you could automate the way you load, configure, and process these prompts—all while integrating your own business-specific variables? With n8n, an open-source workflow automation tool, you can take full control of your AI prompt pipeline. This article walks you through a dynamic n8n workflow that retrieves prompt templates from GitHub, replaces variables automatically, and passes them to an AI agent for content generation.
   
   Overview
   
   The workflow "Load Prompts from GitHub Repo and Auto Populate n8n Expressions" exemplifies a robust automation pattern. It is built to:
   
   - Load a Markdown-based prompt template from a GitHub repository
   - Replace placeholder variables (like {{ company }}, {{ product }}, or {{ sector }}) with real-time context
   - Validate the presence of all required variables
   - Dynamically forward the completed prompt to an AI agent for content generation
   
   This method is especially useful for SEO-focused agencies, content creators, and software teams building scalable generative AI systems.
   
   Workflow Breakdown
   
   1. Manual Trigger: Start Everything on Click
   The workflow begins with a Manual Trigger node, enabling you to test or initiate the process manually within n8n.
   
   2. Set Your Variables
   The ‘setVars’ node defines your working context: organization, repository, prompt path, and variables like company, product, features, and sector. Example entries include:
   
   - company: “South Nassau Physical Therapy”
   - product: “Manual Therapy”
   - sector: “physical therapy”
   
   These values will eventually replace the templated placeholders in the Markdown file.
   
   3. Fetch Prompt from GitHub
   The GitHub node connects to a specified public or private repository, downloading a Markdown file (e.g., SEO/keyword_research.md). Authentication is managed through GitHub credentials stored in n8n.
   
   4. Extract and Read Prompt Text
   Once the prompt is fetched, the Extract from File node parses the file for textual content. The SetPrompt node then stores this content into a variable for further processing.
   
   5. Validate Required Variables
   Before proceeding, the workflow checks that all placeholders in the prompt have corresponding values. The Check All Prompt Vars Present node performs this validation by:
   
   - Extracting all variable names using a regex search for double-curly braces ({{ }}).
   - Comparing these against the variable names defined earlier in the workflow.
   - Logging and returning any missing variable names.
   
   6. Branch on Validation Outcome
   The If node determines the next step:
   
   - If all required variables are present, the workflow continues to replace them in the prompt.
   - If not, the Stop and Error node halts execution and outputs the missing keys to help with troubleshooting.
   
   7. Replace Variables in Prompt
   The replace variables node runs a custom JavaScript function to substitute all placeholders using a dynamic replacements object. This ensures prompts like:
   
   “Write an SEO blog post for {{ company }} promoting {{ product }} in the {{ sector }} industry…”
   
   ...become:
   
   “Write an SEO blog post for South Nassau Physical Therapy promoting Manual Therapy in the physical therapy industry…”
   
   8. Send Completed Prompt to AI Agent
   Once the final prompt is assembled in Set Completed Prompt, it is fed into a LangChain AI Agent powered by Ollama, which interprets the prompt and generates a result.
   
   9. Output AI Response
   The Prompt Output node finalizes the result into a structured format (JSON), making it easy to log, display, or pass along to another system via webhook, API call, or email.
   
   Use Cases and Benefits
   
   1. SEO Content Automation
   For digital agencies maintaining prompt templates for clients, this solution drastically cuts down the time spent editing base content.
   
   2. Personalized Email Drafting
   Load email templates filled with placeholders and dynamically generate personalized outreach sequences.
   
   3. AI Training Prompts
   Easily feed customized variants of prompts into AI systems, useful for fine-tuning or zero-shot task execution.
   
   4. Scalable Workflows
   This approach turns static prompt documents into programmatic content pipelines, easily triggered with API integrations or event-based triggers (e.g., a new task in Trello or CRM).
   
   Conclusion
   
   This n8n workflow bridges static prompt templates and dynamic personalization with AI, all without requiring advanced programming skills. By leveraging GitHub APIs for version-controlled content and LangChain/Ollama for intelligent processing, you open the door to scalable, repeatable automation.
   
   As businesses look to turn AI from novelty to ROI-driven tool, this type of automation represents the future of intelligent content systems. Whether you're an SEO specialist, product marketer, or indie AI engineer, it's time to automate your prompts and scale your creative output with n8n.
   
   Try adapting this workflow with your own prompts and variables today—and experience the power of no-code meets next-gen AI.

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.