ReflectWorld: MM: An Entity-Oriented Multimodal Memory System for Open-Ended Video Stream

by addxai · Codex Skill · ★ 103

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install ReflectWorld

npx @reflectworld/reflectworld start
npx @reflectworld/reflectworld configure

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 61/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentscomputer-visionlifelong-learninglong-term-memorymultimodal-aiopenclawvideo-understandingvisual-memory

Quick Facts

Stars103
Forks6
LanguageTypeScript
CategoryCodex Skill
LicenseApache-2.0
Quality Score61/100
Last Updated2026-07-20
Created2026-07-13
Platformsnode
Est. Tokens~20k

About ReflectWorld

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.

Compatible Skills

These tools work well together with ReflectWorld for enhanced workflows:

  • openclaw-engram — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (61%)
  • memX — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (61%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Codex Skill tools