Load Prompts from GitHub Repo and Auto-Populate n8n Expressions

Workflow Name

Load Prompts from GitHub Repo and Auto-Populate n8n Expressions

Key Features and Highlights

This workflow automatically loads text prompts from a specified GitHub repository and intelligently identifies variable placeholders within them. It then automatically replaces these placeholders using a predefined set of variables to ensure the prompt content is complete and accurate. The workflow includes a variable integrity validation mechanism that halts execution and returns an error message if any required variables are missing, thereby ensuring the accuracy of subsequent processing. Finally, the fully populated prompt is passed to an AI agent (LangChain agent) for intelligent processing, supporting integration with the Ollama language model to enable efficient natural language generation or analysis.

Core Problems Addressed

• Automates the dynamic retrieval of the latest text prompts from a code repository, avoiding errors and inefficiencies caused by manual copy-pasting.
• Supports intelligent variable replacement for templated prompt customization, enhancing prompt reusability and flexibility.
• Ensures all required variables are present through validation, preventing runtime errors due to missing critical parameters.
• Seamlessly integrates with AI language models to facilitate rapid implementation of prompt-based intelligent text generation or task execution.

Use Cases

• Marketing, SEO, and content creation: Automatically fetch prompt templates for keyword research, copywriting, etc., and generate personalized content tailored to specific clients or products.
• Software development or data analysis teams: Dynamically load configuration scripts or prompt templates to enable automated script generation and execution.
• Any automation scenario requiring dynamic content generation based on template text combined with variables.

Main Workflow Steps

1. Manually trigger the workflow start.
2. Predefine GitHub username, repository name, file path, and variable information in the setVars node.
3. Use the GitHub node to retrieve the prompt file content from the specified path.
4. Extract text data using the Extract from File node.
5. Assign the extracted content to the SetPrompt node to prepare for variable validation.
6. The Check All Prompt Vars Present node dynamically extracts variable names from the prompt and verifies their presence in the predefined variables.
7. Based on validation results, if variables are missing, trigger the Stop and Error node to terminate the workflow and report the error; if complete, proceed to the replace variables node to substitute template variables.
8. Store the fully populated prompt in the Set Completed Prompt node.
9. Pass the prompt to the AI Agent node, which calls the LangChain agent and integrates with the Ollama Chat Model for AI language interaction.
10. Output the final result through the Prompt Output node.

Systems and Services Involved

• GitHub: Serves as the storage and version control platform for prompt templates.
• n8n Core Nodes: Including manual trigger, variable setting, text extraction, conditional checks, code execution, and custom error termination.
• LangChain AI Agent: Enables intelligent prompt parsing and text generation.
• Ollama Language Model: Provides natural language processing capabilities as the AI backend model.

Target Audience and Value

• Content creators and marketers: Automate the generation of personalized marketing copy and keyword research reports.
• Automation developers and operations engineers: Quickly build template-based automated workflows to improve efficiency.
• AI product managers and data scientists: Easily manage and dynamically invoke prompt templates to optimize AI interaction experiences.
• Any teams or individuals aiming to combine GitHub-hosted text resources with AI intelligence for dynamic content generation.

In summary, this workflow leverages GitHub’s version-controlled text storage and n8n’s automation orchestration, combined with intelligent variable replacement and AI language models, to greatly simplify the management and application of dynamic prompt content, enhancing the intelligence and automation level of content generation.