Automated LinkedIn engagement workflow. The agent finds a relevant post on your chosen topic, drafts a comment with a genuine insight, gets your approval in chat, and posts it — all in one loop. You approve once before anything is posted. Use when asked to "engage on LinkedIn", "find a post to comment on", "post a LinkedIn comment", or "engage on [topic]". Requires LinkedIn session credentials in .env. Everything runs locally — credentials never leave your machine.
Scanned 9/6/2026
Install to Claude Code
npx -y skills add ZhixiangLuo/10xProductivity --skill linkedin-engagement --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Linkedin Engagement?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/zhixiangluo-linkedin-engagement-10xproductivity)More formats (shields.io, HTML) on the badges page.
---
name: linkedin-engagement
description: Automated LinkedIn engagement workflow. The agent finds a relevant post on your chosen topic, drafts a comment with a genuine insight, gets your approval in chat, and posts it — all in one loop. You approve once before anything is posted. Use when asked to "engage on LinkedIn", "find a post to comment on", "post a LinkedIn comment", or "engage on [topic]". Requires LinkedIn session credentials in .env. Everything runs locally — credentials never leave your machine.
---
> **Canonical:** `workflows/linkedin_automation/linkedin_engagement.md` (in this repo). This file is a thin pointer for Cursor skill discovery — load the canonical workflow doc for the full agent loop, setup, privacy notes, and risk warning.
Read `workflows/linkedin_automation/linkedin_engagement.md` and follow it.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.