LinkedIn

Verified against Claude · 2026-08-07

Turn one long report into five LinkedIn posts that don't feel like the same source recycled

Extract several genuinely distinct, standalone posts from one long report or whitepaper, each built around a different specific finding with its own hook mechanism, and get told honestly when the source only supports fewer strong posts than requested.

ClaudeChatGPTGemini5 fillable variables

The prompt

Ready to copy — highlighted parts are example details you can swap.

THE REPORT OR LONG DOCUMENT
A 12-page industry report on usage-based pricing adoption, with sections on adoption rates, forecasting impact, customer reactions, and reversal case studies

HOW MANY POSTS YOU WANT OUT OF THIS
5

WHO YOU'RE WRITING FOR
RevOps leads and pricing consultants

OVER WHAT TIMEFRAME THESE WILL POST
Spread over 3 weeks, roughly twice a week

WHETHER THE REPORT SHOULD BE LINKED, AND WHERE
Yes, link it — undecided whether in the body or first comment

WHY ATOMIZING A REPORT USUALLY PRODUCES A WEAKENED SERIES INSTEAD OF SEVERAL POSTS
The mistake in turning one long document into multiple posts is treating them as the same argument chopped into pieces — post 2 recognizably a continuation of post 1's framing — instead of as genuinely separate posts that each stand alone. A reader encountering post 3 in their feed almost certainly never saw posts 1 or 2, and a post that only makes sense as part of a series performs like a fragment to everyone except the small number who happened to see the whole sequence in order.

TASK
1. FIND 5 DISTINCT, POST-WORTHY FINDINGS in the source — not sections of the document, findings. A report's table of contents is not a list of good post topics; a good post topic is one specific claim, number, or insight strong enough to justify a reader's attention entirely on its own, wherever in the source it happens to live.
2. FOR EACH FINDING, write a standalone post — its own hook (using a different mechanism each time: a number, a contrarian framing, a specific scenario, and so on, not the same hook style five times), enough context to make sense with zero knowledge of the source report, and a close relevant to RevOps leads and pricing consultants.
3. VARY THE ANGLE, not just the topic — if two findings are both about the same underlying trend, make sure the posts approach it from genuinely different angles (one as a number, one as an implication for a specific role) rather than reading as the same point restated with a different statistic attached.
4. DO NOT let any post reference another one in the set ("as I covered last week," "part 3 of my series on...") — each has to work if it's the only one a given reader ever sees, per Spread over 3 weeks, roughly twice a week, unless you're deliberately building a named series, in which case say so explicitly and structure accordingly instead of defaulting into it.

HONESTY CONSTRAINT
If the source material only genuinely supports fewer distinct, strong posts than 5, say so and give the smaller honest number rather than padding out weak, repetitive posts to hit the requested count.

OUTPUT
The requested number of posts (or the honest smaller number, with the reason), each labeled by which specific finding it's built on, plus a one-line note on where the source report link belongs per Yes, link it — undecided whether in the body or first comment and why that placement over the alternative.

Customize

Optional — swap in your own details for the highlighted parts above.

Why this works

A reader encountering any single post from a series in their feed has, in the vast majority of cases, seen none of the others — LinkedIn's feed is not sequential and doesn't guarantee ordered delivery even to followers, which is the direct reason a post that only makes sense as "part 3" performs like an orphaned fragment to nearly everyone who sees it. The atomization has to produce posts that are actually standalone, not a serialized document with page breaks inserted. Requiring a different hook mechanism per post, and a genuinely different angle rather than the same trend restated with a new statistic, targets the actual failure mode of report-atomization: it is easy to mechanically extract five different numbers from one document and much harder to notice that the resulting five posts all make essentially the same point in the same shape, which reads to anyone following the account closely as a mechanical batch job rather than five separately considered posts. The instruction to give the honest, smaller count if the source doesn't support more good posts protects against the specific incentive this kind of prompt creates: asked for five posts, a model under no other constraint will produce five posts by padding weaker findings up to sound substantial, which is a worse outcome for the account's credibility over a month of posting than publishing three strong ones and being honest that a fourth and fifth weren't there in the source. The instruction to explicitly decide whether this is a deliberate named series or a set of unrelated standalone posts — rather than defaulting into cross-references by accident — matters because the two formats have genuinely different success conditions: a deliberate series can lean on sequence and cumulative context because readers who follow it expect that, while a set of standalone posts sharing a source has to hide that shared origin entirely or it reads as a lazier version of the series format without actually committing to it.

Verified against

Claude Sonnet 5 · 2026-08-07

ChatGPT GPT-5.1 · 2026-08-07

Changelog

  • 2026-08-07 Initial publish for the rewritten linkedin category, verified against Claude (Sonnet 5) and ChatGPT (GPT-5.1).

Need this built into your business?

If a prompt isn't enough — Google Ads management, built and maintained for you — that's Scult's day job.

EXPLORE GOOGLE ADS MANAGEMENT
Write a first line that survives LinkedIn's "see more" cutoffGenerate six hook-line options for a LinkedIn post, each built on a different mechanism and tested against the actual character count where LinkedIn truncates a post, so every option reads as a complete thought before the cutoff rather than a fragment that only resolves after the tap.ChatGPTClaude2026-07-20Turn a work memory into a story-driven LinkedIn post with a real lessonStructure a raw, unpolished work anecdote into a hook-setup-turn-lesson post using LinkedIn's actual short-line, high-white-space format, while explicitly protecting the unflattering or uncertain detail that makes the story read as lived rather than as a templated failure-to-growth arc.ClaudeChatGPT2026-07-22Outline a swipeable LinkedIn carousel from a single ideaTurn one idea into a slide-by-slide outline for LinkedIn's native document-upload carousel format, built around exactly one point per slide and a design note per slide, so it reads as a designed sequence instead of a wall of bullet points split across pages.ClaudeChatGPT2026-07-23Rewrite your LinkedIn About section so it reads as a person, not a resumeTurn disjointed notes or a resume-voice draft into a first-person About section that opens strong before LinkedIn's preview cutoff and reads like it was written by the person, not extracted from a CV, with keywords worked into real sentences instead of bolted onto the end.ClaudeChatGPT2026-07-24
All LinkedIn prompts

Check your AI visibility

One URL in, a 0–100 score and the exact fixes out.

RUN THE CHECK

Browse all the tools

15 tools across six categories
13 of them never send your data anywhere

Free · No signup · No trial clock

SEE THE DIRECTORY