mcpbeat

Linkedin Hook Extractor

sergebulaev/linkedin-linkedin-hook-extractor

Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer).

This is a copy. The original lives at sergebulaev/linkedin-hook-extractor.

2k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
473
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/sergebulaev/linkedin-skills --skill linkedin-hook-extractor

The instruction itself

9 sections, as written by the author

LinkedIn Hook Extractor

Paste a viral LinkedIn post URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template mapped to your topic.

When to use

  • User finds a viral post they want to study
  • User wants to replicate a specific creator's pattern
  • Before linkedin-post-writer to seed a draft with a proven structure

Input

A LinkedIn post URL (any type: activity, share, ugcPost).

Output

  • Formula identified (F1-F16 from ../../references/hook-formulas.md) with confidence score
  • Structural breakdown:
  • Hook lines (first 210 chars)
  • Body architecture (sections + what each does)
  • Close pattern
  • Reaction-triggering devices (numbers, named entities, vulnerabilities)
  • Why it worked psychologically
  • Blank template filled with slot markers matched to the original, ready for the user's voice
  • Cautions: anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)

Steps

  • Parse URL. lib.url_parser.parse_linkedin_url → post_urn.
  • Fetch post body. If APIFY_TOKEN is set, call lib.ApifyClient.fetch_post(url). Otherwise ask the user to paste the text.
  • Classify. Match against the 16 formulas using features:
  • First 2 lines: anaphoric? question? confession? number-led?
  • Body: numbered list? dated receipts? ledger? teardown?
  • Close: mirror question? identity reframe? commitment?
  • F11-F16 cues: in-medias-res emotional scene with no setup (F11 Emotional Cold-Open); "I don't know who needs to hear this" reassurance (F12 Permission Slip); fake-bad-news that resolves positive (F13 Bait-and-Switch); a roll-call of named people thanked (F14 Named Gratitude); "{jargon} explained to kids" glossary (F15 Explain-to-Kids); "outside I'm called X, at home none of it survives" (F16 Status-Strip).
  • Score confidence. If multiple formulas fit, return top 2 with fit scores.
  • Extract structure. Pull each logical section and label it by formula role.
  • Generate blank template. Replace specifics with {slot} markers that match the user's topic.
  • Audit the source. Flag any AI tells in the original so the user doesn't copy them.

Example

See references/examples.md for worked examples.

Formulas reference

See ../../references/hook-formulas.md for the 16 canonical formulas with full skeletons.

Files

  • SKILL.md — this file
  • references/classification-rules.md — feature extraction + scoring heuristics

Related skills

  • linkedin-post-writer — use the extracted template to draft your own
  • linkedin-humanizer --mode audit — audit your draft before shipping

How to use it

Copy the folder

Take sergebulaev/linkedin-linkedin-hook-extractor from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.