The dataset 400 posts that outperformed their authors' baselines, spread across 10 verticals: founders, marketing, engineering, sales, HR, finance, design, data, product, and personal brands. see more"), the structure, the engagement split (comments vs reposts vs likes vs saves), and the author's baseline so a big account's average day did not read as a small account's viral hit.
see more"), the structure, the engagement split (comments vs reposts vs likes vs saves), and the author's baseline so a big account's average day did not read as a small account's viral hit.
This post covers what the data said, what surprised me when I pressure-tested the skills, and where to get them.
The dataset 400 posts that outperformed their authors' baselines, spread across 10 verticals: founders, marketing, engineering, sales, HR, finance, design, data, product, and personal brands.
The page is ready to read now. The fuller skim-friendly version will appear here automatically.
I spent a month doing something slightly unhinged: I pulled 400 above-average LinkedIn posts across 10 verticals, ran them through a fleet of analysis agents, and reverse-engineered why their first lines worked. Then I turned the findings into open-source skills for Claude Code and Codex. This post covers what the data said, what surprised me when I pressure-tested the skills, and where to get them.
Formula Example multiplier Platform Risk Anaphora 4,240 eng R.I.P.
Obituary ("the era of X is over") 3,822 Contrarian + Historical Receipts 3,083 Odd-Precision Money Ledger ("$4,217.38, not ~$4k") 9.4x baseline Paid-vs-Free Reversal 19.64x Year-over-Year Pivot 3.74x Curiosity-Gap Teaser 4.25x Time-Anchor Confession, Self-Proving Meta, Comment-Gate <td...
Open the app view to save this story, compare related coverage, and continue from the same source.