Linkedin-skills linkedin-hook-extractor

Reverse-engineer the hook formula from any viral LinkedIn post. Use when the user finds a post they want to learn from — paste the URL and get a structural breakdown. Identifies which of the 10 canonical 2026 formulas it uses (anaphora, R.I.P. obituary, year-over-year pivot, time-anchor confession, self-proving meta, odd-precision money, paid-vs-free reversal, curiosity-gap, contrarian historical, comment-gate). Returns a blank template you can fill with your own voice. Keywords: hook formula, viral teardown, reverse engineer, post structure, 2026 formulas.

install

source · Clone the upstream repo

git clone https://github.com/sergebulaev/linkedin-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/sergebulaev/linkedin-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/linkedin-hook-extractor" ~/.claude/skills/sergebulaev-linkedin-skills-linkedin-hook-extractor && rm -rf "$T"

manifest: skills/linkedin-hook-extractor/SKILL.md

source content

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Cautions: anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)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

Parse URL.

lib.url_parser.parse_linkedin_url

→

post_urn

Fetch post body. HarvestAPI preferred; fall back to asking user to paste text.
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?
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

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