LinkedIn post scan

Will your post work?

Paste your draft. We compare it to LinkedIn posts from a corpus measured one by one, and email you what, in your text, sounds like a machine rather than like you.

Your post contains

How it works

  1. Step 1

    Paste your draft

    The text you are about to publish, as is. Nothing to connect, nothing to install.

  2. Step 2

    The scan compares it to the corpus

    Your text is filed into measured buckets, then compared to real posts from the corpus.

  3. Step 3

    Your analysis by email

    Your score, the breakdown dimension by dimension, and the writing tics your post shares with a generated text.

Free LinkedIn post scan: will your post work?

The scan reads your draft before you publish it and compares it to 1,763 LinkedIn posts measured one by one, from 165 authors. It first looks for what gives away generated writing: four tics whose excess clearly separates our own texts from those written by hand. Then it measures what your post's format is worth on the corpus. No language model is involved, the calculation is deterministic, and two scans of the same text give the same result. Free, no account, and your text is not kept.

By Maxence Vroilant, founder of Never Boring AIUpdated on .

The score compares your post to 1,763 LinkedIn posts measured one by one, not to an opinion about what works.

What the corpus says

The same numbers a scan uses. Some are counter-intuitive, and we do not smooth them out.

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What each post length is worth

1,050 characters

5.0 / 10

  • under 4005.0
  • 400 to 8005.0
  • 800 to 1,4005.0
  • 1,400 to 2,2005.0
  • over 2,2005.0

Measured on 708 posts in this band, from 157 authors. Length alone does not make the score: it is the dimension that weighs least.

How the score works

Four questions, and how we answer each one. Open the one you care about.

The scan does not read your text the way a reader does. It extracts observable properties: the character count, the length of the first line, whether there are blocks separated by an empty line, the median sentence length, how the post ends, the type of media attached to it.

Each of these properties files your post into a bucket. « First line under 60 characters » is a bucket. « Ends with an invitation to comment » is another. The corpus holds enough posts for each bucket to have its own measured performance: how many reactions and comments the posts falling into it get, on average.

Your score for a dimension is the performance of your bucket in that dimension. The overall score is the average of those scores, weighted by what each dimension actually separates.

A writing tic is not a mistake. It is a habit that comes back too often, and a habit can be changed in one line.
1,763
posts measured
165
auteurs
7
dimensions

The seven dimensions, and what each one weighs

The weights are measured on the corpus, not chosen. They move as the corpus grows. Calibrated on August 28, 2026.

Hook

What it looks at
The mechanics of the first line, the one that decides whether you read on.
Measured weight
43.5 %
Evaluated here
No, needs an account

Substance

What it looks at
The topic covered and how the argument unfolds.
Measured weight
30.6 %
Evaluated here
No, needs an account

Post type

What it looks at
The family of the post: story, advice, stance, behind the scenes.
Measured weight
15.1 %
Evaluated here
No, needs an account

Media type

What it looks at
Image, carousel, video, or text only.
Measured weight
10.8 %
Evaluated here
Yes

Call to action

What it looks at
How the post closes: question, statement, invitation.
Measured weight
0.0 %
Evaluated here
Yes

Form

What it looks at
The breakdown: airy blocks, sentence length, lists, first line.
Measured weight
0.0 %
Evaluated here
Yes

Character count

What it looks at
The length bracket your post falls into.
Measured weight
0.0 %
Evaluated here
Yes

What we get asked
the most

The questions that keep coming up about the scan, answered without rounding off.

Yes, and without an account. You paste your text, you give a first name and an email to open the result, that is all. There is no card and no limit to how many posts you can scan.

The method

Never Boring AI is an AI agent for LinkedIn, built by Maxence Vroilant. The corpus this scan uses as a reference is collected one post at a time, with its real counters, then cleaned: sponsored posts are set aside, company pages too, and lines whose counters could not be read reliably drop out of the measurement.

We publish what the corpus allows us to say, and we write down what it does not. A dimension that separates nothing does not enter the score. A pattern that does not hold on a held-out set is discarded. A number we do not measure, such as reach, is not shown. It is a smaller base than the ones advertised elsewhere, and that is a choice: we prefer a verifiable number to a large unverifiable one.

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Already used by
138 founders