Agent in a Box

Multi-Platform Content Transformation & Repurposing Agent

marketing

Multi-Platform Content Transformation & Repurposing Agent

Problem Statement

Startups and B2B marketing teams face a "content decay" problem: they invest thousands of dollars into high-quality long-form assets that receive a single spike of traffic and then disappear. The manual effort required for content repurposing automation for LinkedIn, X (Twitter), and newsletters is immense.

The specific pain point is the "Context Gap." Generic AI tools often lose the brand voice of the original source. Startups need an AI agent for content creation that automatically ingests a finished long-form asset and outputs a structured "Distribution Pack." Without this, teams leave 80% of their content's potential ROI on the table. This system functions similarly to a Content Repurposing Agent but with advanced multi-platform logic.

What the Agent Does/Doesn't Do

What it does:

  • Ingests YouTube URLs, MP4s, or Blog URLs.
  • Extracts "Atomic Ideas"—the core unique insights.
  • Rewrites content into platform-specific formats using an ai content workflow.
  • Generates image prompts for Midjourney/DALL-E.
  • Suggests timestamps for "viral" video clips.

What it doesn't do:

  • It does not post directly without human approval (Human-in-the-loop).
  • It does not perform video editing.
  • It does not conduct original research outside the provided source.

Workflow

  1. Ingestion & Transcription: Agent monitors a Google Drive folder. It uses Whisper or Deepgram to transcribe audio/video. (Input: URL/File -> Output: Raw Text).
  2. Atomic Idea Extraction: The agent identifies 5-7 standalone "nuggets" of value. (Input: Raw Text -> Output: Structured JSON).
  3. Platform Adaptation: The agent applies specific "Style Blueprints" for automated social media content. (Input: Atomic Idea -> Output: LinkedIn Post, X Thread).
  4. Creative Brief Generation: Agent generates DALL-E 3 prompts for visuals. (Input: Social Copy -> Output: Image Prompts).
  5. Staging & Notification: Outputs are pushed to a Meeting Summary Agent style dashboard or Notion for review.

Tool Stack

  • Make.com - Orchestration layer to connect tools.
    • Pricing: Credit-based; Free tier available (1,000 credits/mo). Extra credits ~$1.00/1,000. (Pricing) ✓ Verified 2026-01-11
    • Documentation | Quickstart
  • OpenAI GPT-4o - Primary engine for extraction and rewriting.
  • Deepgram - For high-accuracy technical transcription.
  • YouTube Data API - For ingesting video metadata and content.
    • Pricing: Free within quota limits. (Documentation) ✓ Verified 2026-01-14
  • Whisper [Unverified] - OpenAI's transcription model.
  • Airtable/Notion [Unverified] - As the Content Management System (CMS).
  • AssemblyAI [Unverified] - For advanced speaker identification in webinars.
  • Midjourney/DALL-E [Unverified] - For visual asset generation.

Quick Integration

Deepgram: Transcribe Audio for Repurposing

import asyncio
from deepgram import DeepgramClient, PrerecordedOptions

async def transcribe_content():
    DEEPGRAM_API_KEY = "YOUR_DEEPGRAM_API_KEY"
    deepgram = DeepgramClient(DEEPGRAM_API_KEY)
    AUDIO_URL = {"url": "https://static.deepgram.com/examples/interview_segments_short.wav"}

    options = PrerecordedOptions(
        model="nova-2",
        smart_format=True,
        punctuate=True
    )

    response = deepgram.listen.prerecorded.v("1").transcribe_url(AUDIO_URL, options)
    print(response.results.channels[0].alternatives[0].transcript)

if __name__ == "__main__":
    asyncio.run(transcribe_content())

Source: Deepgram Docs

OpenAI: Extract Atomic Ideas

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY_HERE")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "Extract 5 standalone 'Atomic Ideas' from this transcript for social media repurposing. Output as JSON."},
        {"role": "user", "content": "TRANSCRIPT_TEXT_HERE"}
    ],
    temperature=0
)
print(response.choices[0].message.content)

Source: OpenAI API Reference

Prompt Skeletons

(Existing prompt skeletons would be placed here)

Implementation Details

⏱️ Deploy Time: 15–25 minutes (Make.com, intermediate)

✅ Success Checklist

  • Google Drive 'Watch Files' trigger successfully detects new MP4/MP3 uploads
  • Deepgram API returns a full transcript with >90% accuracy
  • OpenAI successfully parses the transcript into a structured JSON 'Distribution Pack'
  • Notion/Airtable database populates with separate columns for LinkedIn, X, and Image Prompts
  • Workflow logs show no 429 (Rate Limit) errors during long-form processing

⚠️ Known Limitations

  • File size limits: Make.com and Deepgram may require URL-based processing for files over 100MB instead of direct binary uploads
  • Context Window: Extremely long webinars (>2 hours) may exceed GPT-4o's context window if the full transcript is sent in one prompt
  • YouTube Ingestion: Requires a Public URL or OAuth2 access; private videos cannot be scraped without specific permissions