Linkedin Hook Extractor
/install 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-writerto 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- Cautions: anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)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 filereferences/classification-rules.md— feature extraction + scoring heuristics
Related skills
linkedin-post-writer— use the extracted template to draft your ownlinkedin-post-audit— audit your draft before shipping
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install linkedin-hook-extractor - 安装完成后,直接呼叫该 Skill 的名称或使用
/linkedin-hook-extractor触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Linkedin Hook Extractor 是什么?
Analyze any viral LinkedIn post URL to identify its hook formula, structure, why it worked, and generate a blank template for your own writing. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 85 次。
如何安装 Linkedin Hook Extractor?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install linkedin-hook-extractor」即可一键安装,无需额外配置。
Linkedin Hook Extractor 是免费的吗?
是的,Linkedin Hook Extractor 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Linkedin Hook Extractor 支持哪些平台?
Linkedin Hook Extractor 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Linkedin Hook Extractor?
由 Sergey Bulaev(@sergebulaev)开发并维护,当前版本 v1.0.0。