Turn Your Blog RSS into a Week of Social Posts with n8n and Any LLM API
Posting consistently is a distribution problem, not a writing problem. You already wrote the thinking (the blog post). This workflow turns each new post into platform-native drafts automatically, and a human reviews before anything goes out. The workflow Schedule (daily) -> Read Blog RSS -> Take newest post -> LLM draft -> Parse -> Save to Google Sheets. Building it 1. RSS Read node with your…
Consistency in posting is a distribution issue, not a writing issue. The content has already been created (the blog post). This workflow automatically transforms each fresh post into platform-specific drafts, with a human reviewing before any publication.
The workflow is set to run daily. It begins with reading the blog RSS feed, then selects the most recent item. A Code node extracts just that item. Next, an HTTP Request node sends a POST request to your chosen Large Language Model (LLM) provider, such as OpenAI's gpt-4o-mini. The request follows the provider's chat completions endpoint and includes the model, response format, system message, and user message. The system message instructs the LLM to transform the blog post into three distinct social media drafts.
The LLM's output is then parsed by another Code node. The JSON response is extracted and reshaped into a single item containing the draft dates, original source details, the three generated drafts, and a status tag.
Finally, the drafts are saved to a Google Sheets spreadsheet for review. This serves as the final quality control step, ensuring the posts align with your brand voice and avoiding any embarrassing auto-posting mistakes. The entire process requires only about 10 minutes of human oversight each day. The cost is minimal, particularly if you choose a "mini" model like DeepSeek, which can be significantly cheaper.
This technique is part of a larger set of automation workflows, including lead capture, payment reminders, and reactivation strategies, all with step-by-step setup guides.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
