LinkedIn Hook Extractor (linkedin-hook-extractor)

The linkedin-hook-extractor skill dissects viral LinkedIn posts to isolate and reverse-engineer their core opening mechanics. It strips away topic-specific content, analyzes the psychological triggers operating before the "...see more" cutoff, maps the structure against known formulas (H1-H20), and returns an actionable, fill-in-the-blank template for your own domain.

When to use this skill

Ideal Use Cases:

• Discovering a viral post in your feed and wanting to adapt its opening cadence without copying text.
• Building a personal swipe file of high-converting hook patterns tailored to your industry.
• Diagnosing why a competitor's post generated thousands of engagements and clicks.
• Training team writers or employee advocates on how to structure compelling opening lines.
• Analyzing top-performing historical posts from your own profile to extract your signature formulas.

Disambiguation ('Not for X, use Y'):

• Not for writing a brand new post from scratch -> use linkedin-post-writer.
• Not for removing AI tells from an existing draft -> use linkedin-humanizer.
• Not for converting a YouTube video or tweet thread into a post -> use linkedin-repurposer.
• Not for leaving comments on the post -> use linkedin-comment-drafter.
• Not for monitoring the post's ongoing comments -> use linkedin-thread-monitor.

Anatomy of a Viral Hook: The First 210 Characters

On LinkedIn mobile and desktop, readers only see between 140 and 210 characters before the content is cut off by the "...see more" button.

The hook extractor examines five structural components of the pre-cutoff zone:

1. The Anchor Line: The opening declaration, question, or data point that stops the scroll.
2. The Tension Line: The unexpected contradiction, conflict, or twist that introduces cognitive friction.
3. The Payoff Promise: The hint that clicking "...see more" will deliver the resolution, framework, or lesson.
4. Syntactic Cadence: Sentence length, rhythm, line breaks, and punctuation tempo.
5. Psychological Trigger: Anaphora, Curiosity Gap, Loss Aversion, False Binary, Contrarian Frame, or Social Proof.

The Hook Validation Rubric

Every extracted hook is evaluated against a 4-point diagnostic rubric before template generation:

1. Cutoff Compliance (0–25 pts): Does the critical cognitive tension occur before character 210? If the twist falls on line 4, it fails mobile inspection.
2. Cognitive Tension Index (0–25 pts): Does the opening establish an unresolved puzzle or counter-intuitive claim, or is it merely a predictable statement?
3. Specificity & Credibility (0–25 pts): Does it anchor in verifiable entities, numbers, percentages, or concrete operating roles?
4. Syntactic Velocity (0–25 pts): Can the opening lines be comprehended in under 2.5 seconds of skimming?

How It Works Step-by-Step

When running linkedin-hook-extractor, follow this 5-stage reverse-engineering pipeline:

1. Ingestion & Validation:
   • Ingest the LinkedIn post URL or raw post text.
   • If a URL is provided, validate and extract the post identifier via lib.url_parser.parse_post_url(url).
   • Retrieve or isolate the full text of the post.
2. Cutoff Boundary Extraction:
   • Locate the exact character and line boundary where the "...see more" button is placed (typically line 2 or 3, within 210 characters).
   • Isolate the raw hook text from the remainder of the post body.
3. Mechanical Decomposition:
   • Identify grammatical parts of speech, punctuation anchors, and rhetorical devices.
   • Match the structure against catalog formulas H1-H20 from references/hook-formulas.md.
   • Determine if the hook represents a hybrid formula (e.g. Anaphora + Data-Reveal).
4. Template Abstraction:
   • Replace all domain-specific nouns, verbs, and figures with generalized, bracketed placeholders:
     • [Specific Timeframe / Date]
     • [High-Stakes Metric / Cost]
     • [Conventional Belief / Status Quo Action]
     • [Unvarnished Truth / Counter-Intuitive Outcome]
     • [Actionable Solution / Framework Count]
5. Application Synthesis:
   • Provide two ready-to-use variations of the extracted template applied to different verticals (e.g. Developer Tools, B2B Sales, Founder Journey).

Output Schema

The extractor produces a structured markdown analysis formatted as follows:

### Hook Decomposition
- **Source Post URL / Identifier:** <Target URL or URN>
- **Matched Formula:** H10 (Data-Reveal) / H4 (False-Binary)
- **Character Count (Pre-Cutoff):** 168 characters
- **Primary Psychological Driver:** Cognitive dissonance & specific empirical proof

### Raw Source Hook
> "[Verbatim opening lines of the analyzed post]"

### Generalized Blank Template
```text
[Line 1: Staggering specific number + time period + operational task]
[Line 2: Counter-intuitive breakdown of where the real bottleneck lived]
[Line 3: The unexpected low-tech or simple adjustment that resolved it]

Adapted Domain Examples

• Example A (Software Engineering): ...
• Example B (Enterprise Sales): ...

---

## Approval Gate

Although `linkedin-hook-extractor` is primarily an analytical and drafting skill, whenever the user requests to save the extracted template into their local swipe file or content library, an approval check is triggered:

[HOOK EXTRACTION COMPLETE] Target Analyzed: https://www.linkedin.com/posts/example-founder-activity-7123456789/ Extracted Pattern: H5 (Year-over-Year Pivot) Quality Score: High (Clear contrast, crisp mobile fold)

Action: SAVE TEMPLATE TO REPOSITORY Destination: references/hook-formulas.md (or user swipe file) Template Key: H5_YOY_PIVOT_VARIANT_02

Approval Token: d33918bf-901a-4123-bc99-112233445566 Status: WAITING USER APPROVAL TO PERSIST.

---

## Examples

### Example 1: Deconstructing a Viral Data-Reveal Hook
> **User Input Post:**
> "We analyzed 940,000 cold outreach emails sent by 120 B2B SaaS startups in Q3.
> 88% of emails with more than 3 paragraphs booked zero meetings.
> The top 1% of performers all followed one 40-word structure:"

```text
[EXTRACTOR OUTPUT]

1. Matched Formula: H10 (Data-Reveal) combined with H7 (Controlled A/B)
2. Character Count: 196 characters (safely before mobile cutoff)
3. Psychological Mechanism: Authority through sheer scale of sample size, followed by stark failure metric, ending with elite exclusivity.

4. Generalized Blank Template:
We analyzed [Large Concrete Number] [Units of Work / Attempts] across [Timeframe / Sample Size].
[High Percentage]% of [Standard Way of Doing Task] produced [Zero / Negative Result].
The top [Small Percentage]% of performers all followed [Specific Simple Mechanism]:

5. Adapted Domain Examples:
- Engineering / DevOps:
  "We analyzed 450,000 continuous integration builds across 80 microservice repos last year.
   91% of failed deployments were triggered by misconfigured environment secrets.
   The top 5% of resilient teams all enforced one pre-commit check:"

- Bootstrapped Growth:
  "We analyzed 320 landing page teardowns across early-stage B2B products in 2026.
   82% of pages with more than 2 calls-to-action converted under 1.2%.
   The top 3 converting sites all used one single-column layout:"

Example 2: Deconstructing an Emotional Cold-Open Hook

User Input Post: "At 4:15 PM on a Friday, our lead investor called to tell us the term sheet was pulled. We had 11 days of payroll in the bank account. Here is what happened next:"

[EXTRACTOR OUTPUT]

1. Matched Formula: H3 (Emotional Cold-Open) combined with H17 (Founder Burn-Rate Reckoning)
2. Character Count: 154 characters
3. Psychological Mechanism: Visceral urgency, high stakes, narrative cliffhanger.

4. Generalized Blank Template:
At [Specific Exact Time] on [Day/Date], [Devastating or High-Stakes Event Occurred].
We had [Specific Number] [Days / Dollars / Attempts] left before [Catastrophic Failure].
Here is the exact [Decision / Protocol / Step] we executed:

5. Adapted Domain Examples:
- Product Incident:
  "At 2:10 AM on Cyber Monday, our primary payment gateway began silently returning 500 errors.
   We had 40 minutes before morning European peak traffic hit our servers.
   Here is the emergency failover switch that saved our revenue:"

- Client Retention:
  "At 9:00 AM on Monday, our flagship enterprise customer emailed to cancel their $120k contract.
   We had 48 hours before their executive team finalized next quarter's budget.
   Here is the audit document that turned their cancellation into an expansion:"

Example 3: Deconstructing a Counter-Narrative Obituary Hook

User Input Post: "R.I.P. the 45-minute enterprise demo call (2012–2026). Buyers do not want your sales engineering pitch deck anymore. They want interactive sandboxes within 30 seconds of sign-up. Here is how our pipeline conversion doubled when we killed the deck:"

[EXTRACTOR OUTPUT]

1. Matched Formula: H6 (R.I.P. Obituary) combined with H15 (Counter-Narrative)
2. Character Count: 204 characters
3. Psychological Mechanism: Defiance of legacy sales doctrine, industry obituary intrigue, concrete commercial upside.

4. Generalized Blank Template:
R.I.P. [Widespread Practice / Legacy Tool] ([Start Year]–2026).
[Target Buyers / Practitioners] do not want [Conventional Deliverable] anymore.
They want [Modern Self-Service or Frictionless Alternative] within [Short Timeframe].
Here is how our [Key Business Metric] [Doubled / Cut in Half] when we eliminated [Legacy Practice]:

5. Adapted Domain Examples:
- DevOps / Infrastructure:
  "R.I.P. the multi-week staging environment setup (2015–2026).
   Engineers do not want to wait for DevOps tickets to test code anymore.
   They want ephemeral pull-request preview environments generated in 20 seconds.
   Here is how our shipping cycle dropped from 14 days to 4 hours:"