# Linkedin Hook Extractor

> LinkedIn Hook Extractor

- Skill: `jhamidun/linkedin-hook-extractor` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add jhamidun/linkedin-hook-extractor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jhamidun/linkedin-hook-extractor/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: JHamidun (https://skillmd.com/u/jhamidun)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/jhamidun/linkedin-hook-extractor

---


# 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 (Jake Ward, Lara Acosta, etc.)
- 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-F10 from `linkedin-post-writer/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- **Cautions:** anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)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.** HarvestAPI preferred; fall back to asking user to paste text.
3. **Classify.** Match against the 10 formulas using features:
   - First 2 lines: anaphoric? question? confession? number-led?
   - Body: numbered list? dated receipts? ledger? teardown?
   - Close: mirror question? identity reframe? commitment?
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

> **Input:** `https://www.linkedin.com/posts/dharmesh_every-b2b-software-company-is-or-should-activity-7448808898326654978-iW20`

> **Output:**
> - **Formula:** F10 Contrarian + Historical Receipts (confidence 0.72). Secondary: F5 Self-Proving Meta (0.28).
> - **Hook (first 210 chars):** "Every B2B software company is (or should be) building an agentic version of their product."
> - **Body:** single bold claim → 3 paragraphs of reasoning → specific list of product changes required
> - **Close:** implicit call to action ("Seen this play out in your market yet?")
> - **Blank template:**
>   ```
>   Every {category} {bold claim}.
>
>   {Reasoning paragraph 1 — the forcing function}
>   {Reasoning paragraph 2 — what it requires}
>   {Reasoning paragraph 3 — what breaks if you don't}
>
>   {Closing question that invites reader to take a side}
>   ```
> - **Cautions:** none (post is clean)

## Formulas reference

See `linkedin-post-writer/references/hook-formulas.md` for the 10 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-post-audit` — audit your draft before shipping