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 Customization with n8n: Load and Populate AI Prompts from GitHub
   
   Meta Description:  
   Learn how to automate the retrieval and customization of AI prompts using n8n, GitHub, and LangChain's AI tools. This workflow dynamically loads prompt templates from a GitHub repo, checks for missing variables, replaces them, and feeds them into an AI agent.
   
   Keywords:  
   n8n, GitHub API, AI automation, prompt engineering, LangChain, Ollama, workflow automation, low-code AI, prompt variable replacement, prompt templating, AI agent, dynamic prompts, chat model, open source, no-code tools
   
   Third-Party APIs Used:
   
   1. GitHub API (via n8n GitHub node)
   2. LangChain AI Agent (via LangChain Agent and Chat Model nodes)
   3. Ollama API (via n8n-compatible LangChain Ollama integration)
   
   Article:
   
   Automating Prompt Customization with n8n: Load and Populate AI Prompts from GitHub
   
   In the world of AI-powered content generation, the quality and format of prompts directly influence the output. But managing large sets of prompts with dynamic variables can be tricky, especially when working across teams or handling multiple clients. That’s where the power of n8n, a low-code workflow automation tool, shines. In this article, we’ll explore a workflow that dynamically loads markdown-formatted prompts from a GitHub repository, validates required variables, replaces them, and sends the final prompt to an AI agent for execution—all with just a few clicks.
   
   Let’s break down how this workflow operates and the value it brings to developers and non-technical users alike.
   
   🧩 Workflow Overview
   
   This n8n workflow, titled “Load Prompts from GitHub Repo and Auto Populate n8n Expressions,” simplifies and automates the following steps:
   
   1. Fetch a prompt template stored in a GitHub repository.
   2. Extract the text from the markdown file.
   3. Dynamically inject company/product-specific variables defined in the workflow.
   4. Validate that all required variables are present.
   5. Send the fully populated prompt to an AI agent for execution.
   
   All of this is done through an intuitive sequence of pre-configured nodes without writing a line of shell code or manually managing your AI interactions.
   
   🔧 Step-by-Step Breakdown
   
   1. Manual Trigger for Execution
   
   The workflow begins with a “Manual Trigger” node that allows users to test it interactively. By clicking “Test,” the workflow springs into action.
   
   2. Setting Custom Variables
   
   A node called setVars defines the context under which the AI should operate:
   - Company: South Nassau Physical Therapy
   - Product: Manual Therapy
   - Features: pain relief
   - Sector: physical therapy
   
   This allows you to tailor AI prompts with zero-code personalization.
   
   3. Pulling Files from GitHub
   
   Using the GitHub API node, the template file—keyword_research.md, stored under the SEO/ directory of a public repo—is retrieved. The GitHub repo used here is called PeresPrompts, with the owner TPGLLC-US.
   
   The node dynamically forms the file path using n8n expressions like {{ $json.path }}{{ $json.prompt }}, eliminating the need to hardcode file paths.
   
   4. Extracting the Prompt Text
   
   The Extract from File node reads the markdown file as plain text, making its contents available for further processing in downstream nodes.
   
   5. Validating Prompt Variables
   
   Here’s where the workflow showcases its intelligence.
   
   A custom code node ("Check All Prompt Vars Present") scans the prompt for any placeholder variables written in the form {{ variableName }}. It extracts all such instances, removes duplicates, and checks whether all required variables are provided in the setVars node.
   
   If any are missing, a conditional “If” node redirects the flow to a Stop and Error node, showing the missing keys (e.g., "Missing Prompt Variables : ['company', 'features']").
   
   6. Replacing the Placeholders
   
   Once validation passes, the replace variables code node takes over. It replaces placeholders found in the prompt with corresponding values from the setVars node using a custom JavaScript function.
   
   For example:
   
   Template prompt:  
   “Introduce {{ company }}’s new product in the {{ sector }} space with a focus on {{ features }}”
   
   After replacement:  
   “Introduce South Nassau Physical Therapy’s new product in the physical therapy space with a focus on pain relief”
   
   7. Ready, Set, AI!
   
   After the prompt is completed, it’s passed to the LangChain AI Agent node via the Set Completed Prompt node. This agent is configured to use the prompt for further language tasks like content generation, summarization, or SEO optimization.
   
   The AI Agent can leverage different models. In this case, it's connected with an Ollama Chat Model, which provides the actual natural language output.
   
   8. Output the Result
   
   The final output generated by the AI is stored in a node called Prompt Output, enabling it to be logged, saved, or further processed in another system.
   
   📦 Why This Workflow Matters
   
   This setup is highly modular and scalable. You can:
   - Add more prompt templates with unique variable requirements.
   - Extend the system to support multiple clients or languages.
   - Connect with other LLMs or deploy AI agents across industries like healthcare, SaaS, education, and marketing.
   
   For content creators, marketers, or AI enthusiasts, this pipeline saves time, reduces human error, and integrates seamlessly into larger automation ecosystems.
   
   🧠 Bonus Feature: Sticky Notes
   
   To make the workflow even more user-friendly, several “Sticky Note” nodes are sprinkled throughout with tips and contextual messages, helping future editors or collaborators understand what’s happening and why.
   
   ✨ Final Thoughts
   
   This n8n-based automation demonstrates how you can bridge the gap between static prompt templates and dynamic AI execution—all powered by GitHub, LangChain, and customizable n8n logic. Whether you're building internal AI tools, client deliverables, or just exploring automation possibilities, this is a powerful pattern worth replicating.
   
   Feel inspired? Fork the GitHub repo, plug it into your own n8n instance, and begin scaling your AI prompt workflows today.
   
   — End —

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.