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 sunmughan · Agent Tool · ★ 74
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is meta-automation safe to install? View the security audit →
Agentic Automation 🤖 Repository URL compatibility: the GitHub slug remains . The product is no longer positioned as a conventional "Meta Automation" bot. It is an end-to-end agentic automation runtime that learns who it represents, observes live browser state, reasons about what should happen next, executes one action, verifies the result, and continues from fresh evidence. Built by CodeAir Software Solutions. What this project is now This is a personal/business operating agent for browser-based work. Instead of maintaining a separate bot for every website, the system provides one agentic execution plane: Your Identity + Business Context ↓ Agentic Brain / Memory ↓ Live Browser Ob
| Stars | 74 |
| Forks | 22 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.8638216553993/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-01 |
| Created | 2026-09-19 |
| Platforms | browser, node |
| Est. Tokens | ~18k |
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meta-automation is Autonomous AI Lead Generation, Social Discovery & Smart Conversational Growth Agent for Threads & Instagram. Multi-platform with native browser CDP.. It is categorized as a Agent Tool with 74 GitHub stars.
meta-automation is primarily written in JavaScript. It covers topics such as ai-agents, autonomous-agents, growth-hacking.
You can find installation instructions and usage details in the meta-automation GitHub repository at github.com/sunmughan/meta-automation. The project has 74 stars and 22 forks, indicating an active community.
meta-automation is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to meta-automation on Agent Skills Hub include threads-skills, skills, social-media-caption-generator-claude. 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: