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 ZJU-REAL · MCP Server · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Easel safe to install? View the security audit →
   简体中文 · English 你的私人、持续进化的社媒运营助手 从一个想法开始，完成发现、策划、创作、发布与复盘。 <img src="https://img.shields.io/badge/Easel-ProjectPage-F05A3C?style=flat-square&logo=googlechrome&logoColor=white"
| Stars | 1,256 |
| Forks | 181 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 57.6458486380309/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-20 |
| Created | 2026-08-28 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
These tools work well together with Easel for enhanced workflows:
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Easel is An open-source AI agent for social media — discover trends, create content, publish everywhere, and learn what works across Xiaohongshu, Douyin, Zhihu, Bilibili, and more.🎨一个开源的 AI 社交媒体智能体——发现热点趋势、创作. It is categorized as a MCP Server with 1.3k GitHub stars.
Easel is primarily written in Python. It covers topics such as agent, agent-skill, content-automation.
You can find installation instructions and usage details in the Easel GitHub repository at github.com/ZJU-REAL/Easel. The project has 1.3k stars and 181 forks, indicating an active community.
Easel is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Easel on Agent Skills Hub include PPTAgent, lime, wechat-article-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: