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 coding-by-feng · Codex Skill · ★ 82
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-agent-session-center safe to install? View the security audit →
AI Agent Session Center A lightweight, real-time dashboard that replaces heavy IDEs like VS Code and JetBrains for AI agent workflows. Monitor and control all your Claude Code, Gemini CLI, and Codex sessions from one place — with live SSH terminals, prompt history, tool logs, and queuing. Every session spawns an animated 3D robot in an interactive cyberdrome that visually reflects what the agent is doing. Runs on any device, anywhere. Demo Live demo → aasc.work/demo Desktop <img src="static/screenshot-project-tab-compact.png" alt="Split view w
| Stars | 82 |
| Forks | 9 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 72.9916675777487/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-28 |
| Created | 2026-02-11 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~18k |
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ai-agent-session-center is Real-time dashboard for monitoring Claude Code, Codex, and other AI coding agents—with animated 3D robots, live terminals, tool activity, prompt queues, and session resume.. It is categorized as a Codex Skill with 82 GitHub stars.
ai-agent-session-center is primarily written in TypeScript. It covers topics such as agent-dashboard, ai, ai-agents.
You can find installation instructions and usage details in the ai-agent-session-center GitHub repository at github.com/coding-by-feng/ai-agent-session-center. The project has 82 stars and 9 forks, indicating an active community.
ai-agent-session-center is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-agent-session-center on Agent Skills Hub include deepcontext-mcp, argus, daymon. 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: