by rokpiy · MCP Server · ★ 587
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is auto-commenter safe to install? View the security audit →
Auto-Commenter A marketing automation framework using Claude Skills and Playwright MCP for authentic community engagement. Features personalized comment generation, target community selection, daily tracking, batch execution, and lead identification. Currently supports Reddit — easily extensible to Twitter, LinkedIn, Discord, and other platforms by adding personalization files. What is This? Auto-Commenter is a Claude AI skill that: Learns your writing style from your actual comments Analyzes posts deeply to understand context and intent (not keyword-based) Writes authentic comments that pass 16-point quality che
git clone https://github.com/rokpiy/auto-commenter.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 587 |
| Forks | 115 |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.7694644264973/100 |
| Last Updated | 2026-03-15 |
| Created | 2026-01-28 |
| Platforms | claude-code, mcp |
| Est. Tokens | ~121k |
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auto-commenter is A Claude skill that automatically posts personalized, authentic comments in your target communities.. It is categorized as a MCP Server with 587 GitHub stars.
The README on GitHub (github.com/rokpiy/auto-commenter) gives: git clone https://github.com/rokpiy/auto-commenter.git. Usage details are in the same README.
auto-commenter is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to auto-commenter on Agent Skills Hub include mcphub.nvim, octocode-mcp, Overture. 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: