PlaybookPrompts

Turn a raw professional opinion into a structured LinkedIn post

Writing & Content linkedinthought-leadershipghostwriting

Most professionals have strong opinions from their work but struggle to shape them into posts that read well and hold attention past the first line. This prompt takes a rough point of view and structures it into a LinkedIn post with a clear arc.

Prompt
You are a ghostwriter who specializes in professional thought leadership. Turn the raw opinion below into a LinkedIn post using these steps.

1. CORE CLAIM: State the author's main argument in one sentence. This is the post's spine—every paragraph must connect back to it.
2. HOOK OPTIONS: Write three opening lines (no more than 12 words each). Each must create a reason to keep reading without resorting to vague teases or clickbait questions. Label them A, B, C.
3. BODY DRAFT: Using Hook {{CHOSEN_HOOK}} (A, B, or C—choose the strongest if the user has not specified), write the full post body. Structure:
   - Hook (1 line)
   - Context or tension that makes the claim matter (2–3 lines)
   - The core argument with one concrete example or observation (4–5 lines)
   - One implication or practical takeaway (2–3 lines)
   - Closing line that does not end with a generic question
4. FORMAT CHECK: Confirm the post is between 150–280 words, uses no bullet points, and contains no hashtags (unless the author explicitly wants them).

Raw opinion or note:
{{RAW_OPINION}}

Author's role and industry (for voice calibration): {{AUTHOR_CONTEXT}}

Note: This prompt is for first-person posts. If writing in third person for a brand account, flag that upfront and adjust pronouns in Step 3.
Variables to fill in
  • {{CHOSEN_HOOK}}
  • {{RAW_OPINION}}
  • {{AUTHOR_CONTEXT}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{CHOSEN_HOOK}}{{RAW_OPINION}}{{AUTHOR_CONTEXT}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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