Use Claude or ChatGPT to Write and Publish Social Media Posts

You want to describe a post in plain English -- "write a LinkedIn post about the top 3 AI trends this week and publish it to my account" -- and have it happen. No copying text from ChatGPT into a scheduling tool. No downloading images and re-uploading them. No switching between five browser tabs.

This is the fastest-growing category of automation question across n8n, Make.com, and Claude communities. Users are connecting LLM nodes (OpenAI, Claude, Gemini) to publishing APIs, building AI agents with MCP tools, and packaging reusable Claude Code skills for social media management. The common thread: everyone wants an AI that writes the content AND publishes it, end-to-end.

Here are three ways to set this up, from simplest to most automated.

Option 1: Claude Desktop or Claude.ai with Blotato MCP

The Blotato MCP Server connects Claude to your social media accounts through the Model Context Protocol. You type natural language commands. Claude calls the Blotato API to create visuals, write posts, and publish them.

Setup

1. Connect your social accounts in Blotato Settings.

2. Add the Blotato MCP server to Claude Desktop or Claude.ai (see Setup Guide).

3. Start giving commands.

Example prompts

Research and publish:

Research the top 3 AI trends this week. Write a LinkedIn post summarizing the findings. Generate a carousel image. Publish to my LinkedIn account and schedule to my next free slot.

Repurpose content:

Take this YouTube video [URL] and turn it into 5 platform-specific posts: a Twitter thread, a LinkedIn post, an Instagram carousel caption, a TikTok caption, and a Threads post. Publish all of them.

Daily content:

Read my latest blog post at [URL]. Extract the key takeaways. Write a post for each of my connected platforms. Schedule each post to the next free slot in my content calendar.

Claude handles every step: researching, writing, generating visuals, and publishing. Each step maps to a Blotato API call that Claude makes through the MCP server.

For more example prompts, see: MCP Example Prompts

Option 2: n8n or Make.com with an LLM node

This approach uses a traditional automation workflow with an LLM node for content generation and Blotato nodes for publishing. The workflow runs on a schedule without any manual input.

How it works

1. A trigger fires (daily schedule, RSS feed, Google Sheets update).

2. An LLM node (OpenAI, Claude, or Gemini) generates post text based on a prompt you define.

3. A Blotato Create Visual node generates an image or video.

4. Blotato Publish Post nodes send the content to each platform.

Step-by-step: n8n workflow

1. Install the Blotato community node in n8n.

2. Add a Schedule Trigger node. Set it to run daily.

3. Add an OpenAI node (or Claude, Gemini, etc.). Set the prompt to generate a social media post about a topic. Example system prompt: "You are a social media content writer. Write a short post about [topic]. Write for LinkedIn (professional tone, 3-5 short paragraphs). End with a question to drive engagement."

4. Add a Blotato Create Visual node. Select a carousel or image template. Pass the LLM output into the prompt field so the visual matches the post content.

5. Add a Blotato Get Visual Status node. Poll until status is done.

6. Add Blotato Publish Post nodes for each platform. Pass the LLM-generated text into the text field and the visual URL into mediaUrls.

7. Set useNextFreeSlot: true on each Publish node to schedule posts across your content calendar.

8. Activate the workflow.

Step-by-step: Make.com scenario

1. Add a Schedule trigger module (daily or weekly).

2. Add an OpenAI module (or Claude/Anthropic module). Configure your prompt to generate post text.

3. Add an HTTP module to call POST https://backend.blotato.com/v2/videos/from-templates with a template ID and the LLM output as the prompt. Set inputs to {}.

4. Add a polling loop (Repeater + HTTP GET) to check visual status until done.

5. Add a Router module, then one HTTP module per platform calling POST https://backend.blotato.com/v2/posts. Pass the LLM text and visual URL.

6. Activate the scenario.

Option 3: Claude Code with Blotato skills

Claude Code runs in your terminal and connects to Blotato through the MCP server or direct API calls. You create reusable "skills" -- markdown files that define repeatable workflows.

Example: a /post skill

Create a skill file that Claude Code executes when you type /post:

1. Ask what you want to post about.

2. Research the topic using Blotato's Create Source endpoint with sourceType: "perplexity-query".

3. Write platform-specific posts for each connected account.

4. Generate a visual using the Create Visual endpoint.

5. Publish to all platforms using the Publish Post endpoint with useNextFreeSlot: true.

For a full tutorial on building Claude Code skills for social media, see: Claude Code Integration

Choosing between the three options

Approach	When to use	Runs automatically
Claude + MCP (Option 1)	You want to interact with the AI conversationally. You review and approve each post before publishing.	No -- you initiate each session.
n8n / Make.com + LLM (Option 2)	You want a fully automated pipeline that runs on a schedule. Posts publish without your involvement.	Yes -- runs on a timer or trigger.
Claude Code + Skills (Option 3)	You want reusable commands in your terminal. You type /post and the AI handles the rest.	No -- you initiate each command.

All three approaches use the same Blotato API under the hood. The difference is how you trigger the workflow and whether a human reviews the output before publishing.

Adding a human review step

If you want to review AI-generated content before it goes live:

1. Set isDraft: true when creating visuals. Review and edit them in the Blotato dashboard before publishing.

2. Use useNextFreeSlot: true when publishing. This schedules the post for a future time slot, giving you time to review in the dashboard before it goes live.

3. In n8n or Make, add a Slack/email notification step before publishing. Send the generated content for approval. Use a webhook to receive the approval signal and trigger the publish step.

Writing prompts that produce platform-specific content

The LLM prompt determines the quality of the output. Generic prompts produce generic posts. Here is a structure that works across platforms:

Write a {platform} post about {topic}.

Tone: {professional/casual/educational}
Length: {character limit for the platform}
Format: {paragraph/bullet points/numbered list}
End with: {a question/a CTA/a bold statement}

Context: {paste relevant source material, data points, or talking points}

For automated workflows, store the prompt template in your n8n/Make workflow and inject the topic dynamically from an RSS feed, Google Sheet, or Blotato Source extraction.

Use the Create Source endpoint with sourceType: "text" and customInstructions to rewrite one post for multiple platforms:

{
  "source": {
    "sourceType": "text",
    "text": "Your original post text here"
  },
  "customInstructions": "Rewrite this for Twitter. Keep it under 280 characters. Make the first line a hook."
}

Run this for each platform with different instructions. This replaces having separate LLM calls per platform.

Forum threads referenced

These are the community discussions that prompted this post:

• AI-Generated Social Media Posts from Google Drive to Canva (n8n)

• Free Workflow: AI Content Repurposing -- 1 Blog Post to 4 Platforms (n8n)

• I Automated Instagram Carousel Creation with n8n: One Telegram Message In, 6 Slides Out (n8n)

• 100% AI Powered Automated Social Media System (Make)

• How To Automate Social Media Content Repurposing with Airtable, Dumpling AI, Claude AI and Make.com (Make)

• AI Social Media System Posts to 9 Platforms (Make)

• I Automated My Content Pipeline with Claude Code (DEV Community)