skill/hub

linkedin-hook-extractor

バイラルなLinkedIn投稿のURLからフックの公式をリバースエンジニアリングする。2026年に使われている16の標準的な公式(アナフォラ、R.I.P.、イヤーピボット、タイムアンカー、好奇心のギャップ、逆張り、コメントゲート、感情のコールドオープン、名前付き感謝、その他7つ)、なぜうまくいったのか、そして空白のテ…

@sergebulaevMIT更新 2026-08-22v0.1.0直近30日 0 回
コンテキストを 657 トークン使用しますコンテキストを 657 トークン 使用します

取り込み時のスキャン結果 · 2026-08-22

接続先として検出されたホスト: www.linkedin.com

ルールに基づく静的スキャンの結果です。検出がないことは安全を保証するものではありません。 本文と同梱スクリプトは全文を閲覧できるため、実行前に内容をご確認ください。

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  • Cautions: anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)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

  1. Parse URL. lib.url_parser.parse_linkedin_url → post_urn.
  2. Fetch post body. If APIFY_TOKEN is set, call lib.ApifyClient.fetch_post(url). Otherwise ask the user to paste the text.
  3. 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).
  4. Score confidence. If multiple formulas fit, return top 2 with fit scores.
  5. Extract structure. Pull each logical section and label it by formula role.
  6. Generate blank template. Replace specifics with {slot} markers that match the user's topic.
  7. 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