| name | linkedin-hook-extractor |
| description | 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). |
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- Cautions: anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)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 filereferences/classification-rules.md — feature extraction + scoring heuristics
Related skills
linkedin-post-writer — use the extracted template to draft your ownlinkedin-humanizer --mode audit — audit your draft before shipping
