Your LinkedIn About collapses after 3 lines.
Four questions, a score on 6 measured criteria, real About sections from your field and three rewritten versions. No invented figures, ever.
Paste your profile, we read your About
We read your current About and your job straight off your profile. If it is empty, all the better: that is what the tool is for.
Go and read what others wrote
The library holds 7,225 real About sections, filterable by theme and length. None are generated, all are credited and linked to their profile.
Open the libraryLinkedIn About Generator: score yours out of 100 and get 3 rewrites
By Yannis Haismann, co-founder of LinkPost ·
LinkPost's LinkedIn About generator reads your profile, asks four questions, scores your About on 6 measured criteria and writes 3 complete versions. Benchmarks: 5,913 French-language profiles measured on 1 August 2026, where About sections of 600 characters or less belong to authors at 30 median likes per post, against 22 beyond 2,000. Free, report by email.
TL;DR
- 1LinkedIn collapses the About after 3 lines, roughly 265 characters, while the corpus median is 1,036: three quarters of the text is never read.
- 2Four questions before any generation: who you help, the result you deliver, your hard proof, what the reader should do. Without facts, an AI invents them.
- 3Across 5,913 French-language profiles measured on 1 August 2026, About sections of 600 characters or less belong to authors at 30 median likes per post, against 22 beyond 2,000: a 36% gap.
- 4The 3 versions are ranked by the same scorer used on your current text, never by the model's opinion of its own output.
- 5No figure, client, employer or award is invented: only your own material is reused.
How the LinkedIn About generator works
The tool runs in three moves. You paste your profile URL and we read it. You answer four short questions. The analysis runs, then the report is emailed to you.
That middle step is what separates the tool from an ordinary text generator, and it is not there to slow you down.
Why four questions
A LinkedIn headline can be derived from what you already wrote. A thousand-character About cannot. Given nothing to work with, a model fills the gap with imaginary clients and invented figures, which is exactly what makes most generated text unusable.
Your four answers are therefore the only source of facts allowed: who you help, the result you deliver, a proof you can stand behind, what the reader should do next. The last two are optional, because demanding a figure would be the surest way to have one invented.
The 6-criteria score
Six weighted criteria: what survives the three-line fold (35%), length (25%), the job keyword before the fold (15%), paragraph structure (10%), hard proof (10%) and the closing ask (5%).
The keyword criterion is only evaluated when your profile makes your job identifiable. Without that, the tool would rather skip it than guess. An empty About scores zero, which is the honest reading of an empty section.
The three-line fold preview
Your About is shown collapsed the way LinkedIn collapses it, and the tool tells you how many characters sit below the fold and what share of your text that represents. With a corpus median of 1,036 characters against a visible zone of roughly 265, the answer often lands around 75%.
The wall of examples and the 3 versions
Before generating anything, the tool surfaces About sections currently live on profiles in your field, with the name, photo, profile link and the person's average reach. The search is always scoped to your field, never a global ranking: sorting the whole corpus by reach surfaces media personalities whose About teaches a B2B professional nothing.
The 3 generated versions are deliberately different in structure, not three rewordings of one idea. They are ranked by the score computed on them, with the exact rules applied to your current text.
What the data says about LinkedIn About sections
The benchmarks come from a measurement taken on 1 August 2026 across 5,913 French-language LinkedIn profiles carrying an About section, a verifiable profile URL and a known average reach.
The clearest result concerns length, and it decreases from one bracket to the next without exception.
Length and reach
600 characters or less: 30 median likes per post (1,209 profiles). 601 to 1,200: 25 (1,969 profiles). 1,201 to 2,000: 22 (1,948 profiles). Beyond 2,000: 22 (787 profiles). A 36% gap between the two extremes.
That is why the generated versions target 600 characters and never exceed 900.
What happens before the fold
The first line runs 180 characters at the median, against a visible zone of roughly 265. 43% of profiles place a figure there, and those authors show 26 median likes against 23 for the rest, even though 72% of the corpus carries a figure somewhere in the text.
A real but modest gap, weighted accordingly: it counts inside the fold criterion, it does not decide it alone.
Correlation, not causation
Nothing here proves that writing short earns reach. The authors of short About sections are not the same people as the authors of long ones.
One awkward example, published as measured: the 28% of About sections that address the reader directly inside the visible zone show a lower median reach (21 against 26). Common advice says the opposite. That signal is therefore measured, displayed, and deliberately excluded from the score: most likely a population effect, not a writing rule.
What the tool does with your data
The analysis reads your public profile once, at the moment you paste its URL. Your four answers and the report are kept long enough to send it to you and to let you reopen it from the link in that email.
The report is never shown inline after the analysis: it goes out by email, and that link is what opens it. You therefore watch the analysis finish before anything is asked of you.
The 6 scoring criteria, and what each one weighs
| Criterion | Weight | What is measured |
|---|---|---|
| The fold | 35% | Opening, figure and a complete first sentence inside the 3 visible lines |
| Length | 25% | Position in the measured brackets, from 600 to over 2,000 characters |
| Job keyword | 15% | Whether the term people search you by appears before the fold |
| Structure | 10% | Paragraphs separated by blank lines, length of the longest block |
| Hard proof | 10% | Presence and count of figures |
| Closing ask | 5% | An explicit action or a link in the last 350 characters |
An About section generated without facts is a text that lies politely. That is what the four questions are for.
LinkPost, LinkedIn About generator
Source: Add, edit or remove the About section of your profile (LinkedIn Help)
Frequently asked questions
- How do you write a good LinkedIn About section?
- Put who you help, the result you deliver and your hard proof inside the first 3 lines, roughly 265 characters, because that is all LinkedIn shows before the "see more". Aim for 600 characters in total: that is the bracket tied to 30 median likes per post across the 5,913 profiles measured, against 22 beyond 2,000. Close on an explicit ask, which only 22% of profiles do.
- Is the LinkedIn About generator free?
- Yes. The analysis, the wall of real examples and the 3 generated versions are free. The report is emailed to you: that is the only thing asked in return, and it is only asked once the analysis has visibly finished.
- Why does the tool ask me four questions?
- Because a thousand-character About cannot be derived from a profile. Without facts, a model fills the gap with invented clients and figures. Your four answers are the only source of facts the generation is allowed to use.
- Can the tool invent figures on my behalf?
- No, and it is an explicit constraint of the generation: no figure, client, employer, award or date is fabricated. If you supply no hard proof, the versions are written to work without one rather than invent it.
- How long should a LinkedIn About section be?
- The generated versions target 600 characters and never exceed 900, because across 5,913 measured French-language profiles, About sections of 600 characters or less belong to authors getting 30 median likes per post, against 22 beyond 2,000. That is a correlation, not a guarantee of reach.
- How many characters show before the "see more"?
- LinkedIn collapses the About after 3 lines, roughly 265 characters on a desktop screen and considerably fewer on mobile. It is the heaviest criterion in the score, at 35%.
- Do I need an existing About section to use the tool?
- No. An empty section logically scores zero, and that is precisely the case the tool exists for. The four questions are enough to generate three complete versions.
- Where do the displayed examples come from?
- From public LinkedIn profiles in your field, shown under their author's name with a link to their profile. They are neither rewritten nor generated. The full library of 7,225 About sections is browsable separately, with no email.
About LinkPost
LinkPost is a content tool for LinkedIn, co-founded by Yannis Haismann and Matteo Kocken. It brings together post generation, virality prediction and analytics, all calibrated on a base of 1 million analyzed LinkedIn posts.
Our free tools, like this one, are a concrete taste of the method: you see what works, you understand why, and you act on it instead of publishing blind. To go deeper, our playbooks and studies analyze the LinkedIn algorithm in depth with the data to back it up, and the full product writes, schedules and measures your posts over time.
The logic is the same from one tool to the next. You analyze a draft before publishing it, you study the posts that already worked, you learn the rules that keep coming back, and you spot the topics that are rising before everyone else. Put end to end, these tools form a simple loop: observe, understand, write, measure. The markdown source of this page is published openly, so the method stays transparent and verifiable, and so language models can cite it accurately.
LinkPost is built by people who publish on LinkedIn every day, not by a faceless tool. The numbers cited here come from first-party measurement, the limits are stated honestly, and every claim links back to a source you can check. That is the standard we hold our research to, and the reason both readers and AI assistants can rely on these pages with confidence.