AI Content Agent Workflow for 3X LinkedIn Engagement

Scrape only content that has already proven engagement to remove guesswork.

The workflow pulls 20-30 high-engagement YouTube videos and top X posts for a given keyword, transcribes them, and merges the text into a single context block.

Why: Starting from validated hooks and angles dramatically increases the probability that new content will resonate.

"Instead of starting from scratch or whatever, go and find things that are already working, so you can shortcut that and take those learnings and make it your own."

Use multiple data sources to build deep context before prompting an LLM.

Combine YouTube transcripts, X posts, and your own top-performing content to feed the model a rich dataset.

Why: Most users under-context the model and blame the output; massive context yields massive quality.

"most people, they're opening up Claude or something like that. They're entering in a prompt, and then it spits something out, and they're like, I don't like this. Well, you're not going and scraping all the top performing YouTube videos, tweets, analyzing your own account for brand voice, and stuff like that, and then feeding it into the model."

Instruct the LLM to generate fresh angles, not copy existing content.

After ingesting the scraped data, a dedicated agent is prompted to extract marketing-focused ideas that are inspired by but not duplicative of the source material.

Why: Originality preserves brand integrity while still leveraging proven patterns.

"I don't want to go and just like copy people's content. I just want to use that as inspiration and then come up with my own ideas."

Anchor every idea to a scroll-stopping hook and a unique point of view.

Each generated idea must include a short title, a hook, optimal format, and a differentiated angle for marketers using AI workflows.

Why: Social feeds reward novelty and clarity; without a hook, even great insights are ignored.

"I want to have a scroll-stopping idea for my hook to pull people in."

Back every claim with real stats, trends, or case studies to avoid hallucination.

A separate research agent queries Perplexity for live use-cases, frameworks, and statistics that support the marketing angles.

Why: Data-backed posts build trust and outperform purely opinion-based content.

"I also don't want the model to completely like hallucinate and just come up with like random stuff that doesn't make any sense."

Use the best model for each micro-task rather than one model for everything.

OpenRouter allows switching between Perplexity Sonar for research, Claude 3.7 Sonnet for long-form writing, and GPT-4 for image prompts within a single workflow.

Why: Specialized models outperform generalists; chaining them yields higher quality at lower cost.

"oftentimes in a workflow, I try to use the best LLM or the best model for each specific task."

Explicitly ban AI clichés to maintain authenticity.

Prompts include negative constraints: no hashtags, no emojis, no M-dashes, no corporate jargon, no fluff.

Why: Readers instantly recognize AI tropes and disengage; authenticity increases engagement.

"M dash overload on the internet right now. And like people see that they know it's AI too."

Inject personal narrative and direct address to create human connection.

Instruct Claude to write in first person, speak directly to the reader, and weave in personal anecdotes.

Why: LinkedIn rewards vulnerability and relatability over detached expertise.

"Write with authentic expertise and direct communication. Be confident and straightforward. Don't be overly formal or academic. Speak directly to the reader."