No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by sergebulaev · Claude Skill · ★ 4.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is linkedin-skills safe to install? View the security audit →
LinkedIn Marketing Skills for Claude Code and Codex 10 skills that help Claude Code and Codex write LinkedIn posts, comments, and replies in your voice. They draft content, strip AI tells, and wait for your approval before anything gets published. No coding required. Install Pick whichever way you use Claude Code or Codex: Codex CLI To test a local clone before publishing changes: bash git clone https://github.com/sergebulae
| Stars | 4,009 |
| Forks | 669 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 71.7585377625253/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-03 |
| Created | 2026-04-14 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~17k |
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linkedin-skills is Claude skills for LinkedIn. 11 Claude Code and Codex skills that write human-sounding LinkedIn posts, craft comments that get noticed, analyze your feed, and build a publishing cadence, all from your . It is categorized as a Claude Skill with 4.0k GitHub stars.
linkedin-skills is primarily written in Python. It covers topics such as agent-skill, agent-skills, ai-agents.
You can find installation instructions and usage details in the linkedin-skills GitHub repository at github.com/sergebulaev/linkedin-skills. The project has 4.0k stars and 669 forks, indicating an active community.
linkedin-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to linkedin-skills on Agent Skills Hub include Claude-Code-Everything-You-Need-to-Know, claude-blog, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: