Find top performing LinkedIn post examples

Real post examples are different from templates.

A template suggests a structure such as a list, story, lesson, or contrarian opinion. A real post shows how one author used a structure in a particular context and how readers responded. The example can reveal pacing, line length, specificity, media format, and the relationship between the hook and the rest of the post.

When collecting LinkedIn post ideas, treat each result as evidence of one execution rather than a formula. Do not copy a post or assume its result transfers to another account. Use multiple examples to form a hypothesis, then write an original version grounded in your experience, evidence, and audience. This produces more useful research and avoids turning a source post into a reusable script.

Choose the right ranking for the question.

Engagement signals answer different questions. Select the metric before reviewing the results so a large reaction count does not distract from the behavior you actually want to understand.

Which LinkedIn topics can you explore?

The searchable picker currently includes 60 options: broad professional categories, selected subtopics, and a snapshot of LinkedIn trending topics. Search for subjects such as marketing, sales, leadership, artificial intelligence, workplace trends, or content quality instead of scrolling through the complete list.

Topic collections change over time, and LinkedIn can expose additional deeply nested subjects that are not in this curated picker. Use the free finder for the maintained selection. If you already know another public Top Content URL or topic path, the bulk Apify scraper can accept it directly.

How to evaluate viral LinkedIn posts beyond the engagement count.

Use performance as a filter that identifies posts worth closer inspection. Then compare the creative decisions and the context around them. A large number does not prove that a post was broadly viral or that its format will transfer to your account. A high-performing post from a large creator may be a poor benchmark for a new company page, while a lower-volume example can contain a format that fits a specialist audience.

• Compare the first two lines because the hook determines what readers see before expanding a post.
• Separate the topic from the format: a carousel, text post, image, or article may perform differently even when the subject is similar.
• Check the publication date and author context before treating a high count as a current benchmark.
• Use several examples to identify repeated structures; one viral post is an anecdote, not a reliable content strategy.
• Save the original URL and verify the post before quoting, republishing, or using it in client work.

Export the evidence behind each example.

JSON preserves nested author and media data for code or automation. CSV flattens the key fields for sorting, annotation, and comparison in a spreadsheet. Both formats include the original post URL, text, author, publication date, topic, ranking, reaction count, comment count, repost count, and collection time when those values are publicly available.

Keep published_at and scraped_at separate. The first tells you when the author published the post; the second tells you when Curly collected the public ranking. This prevents an older post found today from being mistaken for a newly published post.

{
  "topic_title": "Marketing",
  "topic_rank": 1,
  "post_url": "https://www.linkedin.com/posts/...",
  "author": { "name": "Example Author" },
  "text": "A real public post...",
  "published_at": "2026-07-30",
  "reaction_count": 1240,
  "comment_count": 86,
  "repost_count": 12,
  "scraped_at": "2026-08-06T10:30:00.000Z"
}

Free example finder versus the bulk Apify scraper.

Use the free page for a quick review of one topic. Use the LinkedIn Top Content Scraper on Apify when your content workflow needs several topics, repeatable collections, larger combined datasets, schedules, or webhooks.