LinkedIn Content System

Most founder LinkedIn posts fail not because the idea is bad but because they lead with the conclusion instead of the tension. "Here's what 10 years taught me about sales" is a promise that rarely gets kept. "We lost a $400K deal because our champion got promoted" is a hook — because it makes you ask: wait, how does a promotion kill a deal? A good insight is usually one of three things: something that sounds wrong until you think about it, something that costs most people more than they realize, or something that reframes a situation everyone has been in but never articulated. Paste this prompt into Claude or ChatGPT. Share a story, a belief, a result, or a conversation. The AI finds the one real insight, produces five posts with distinct formats (The Moment, The Counterintuitive, The Observation, The Framework, The Reframe), and tells you which to post first and why.

1. Click "Copy prompt" above and paste it into Claude or ChatGPT to start the session.
2. Share one piece of raw material: a story from the last 30 days, a belief your industry gets wrong, a specific result with what worked and what didn't, or a conversation that changed how you think about something.
3. Also tell the AI who you're writing for (role, what they're dealing with right now) and what you want them to feel or do after reading.
4. You'll receive five fully written posts — The Moment, The Counterintuitive, The Observation, The Framework, The Reframe — each ready to paste.
5. Review the "Best of the five" recommendation — it names which to post first and the specific reason.
6. Check "What's missing from your raw material" — if any post is weaker than expected, the AI names exactly what would strengthen it.

Input: Story — We had a customer go dark after 3 months of heavy usage. Support ticket volume had actually been increasing, which we thought meant engagement. When they finally replied, they said the product was creating more work than it saved.

Insight identified: High support volume isn't an engagement signal. It's a friction signal. We were misreading our most churned customers as our most engaged.

POST 1 — THE MOMENT:
Hook: Our most "engaged" customer churned after 3 months.

We measured engagement by support tickets. High ticket volume meant people were using the product.

It didn't.

It meant the product was creating work. When they finally replied to our win-back email, they said: "We wanted it to work. That's why we kept trying."

The thing I track now isn't ticket volume. It's whether support tickets decrease after the first month. If they don't, retention won't either.

POST 4 — THE COUNTERINTUITIVE:
Hook: Your most active users might be your highest churn risk.

[Full post body...]

Why this format works: The insight — that activity can signal struggle, not success — sounds backwards until you read it.