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 addxai · Codex Skill · ★ 93
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ReflectWorld safe to install? View the security audit →
ReflectWorld Map the real world into cyber memory. ReflectWorld is live world memory for AI agents. It turns camera streams into persistent visual experience: what happened, who or what appeared, what changed, and what an agent should remember next. Why ReflectWorld · Core Capabilities · Showcase · Benchmark Results · Paper · Quick Start · Runtime Shape · Docs Want to try it first? Jump to Quick Start and run the standalone Dashboard locally. Why ReflectWorld AI agents are becoming fluent in text, code, tools, and APIs. But their visual perception is still mostly episodic: they see isolated clips, answer one question, and forget
| Stars | 93 |
| Forks | 5 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 61.2513627340494/100 |
| Last Updated | 2026-07-20 |
| Created | 2026-07-13 |
| Platforms | node |
| Est. Tokens | ~20k |
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ReflectWorld is ReflectWorld-MM: An Entity-Oriented Multimodal Memory System for Open-Ended Video Stream. It is categorized as a Codex Skill with 93 GitHub stars.
ReflectWorld is primarily written in TypeScript. It covers topics such as ai-agents, computer-vision, lifelong-learning.
You can find installation instructions and usage details in the ReflectWorld GitHub repository at github.com/addxai/ReflectWorld. The project has 93 stars and 5 forks, indicating an active community.
ReflectWorld is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to ReflectWorld on Agent Skills Hub include watch-skill, vidxp, kindly-web-search-mcp-server. 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: