Skill: sergebulaev/linkedin-hook-extractor (version 1.0.0)

--- name: linkedin-hook-extractor description: 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. --- # 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for the user's voice - **Cautions:** anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics) ## Steps 1.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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)