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 LeoYeAI · Agent Tool · ★ 548
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openclaw-auto-dream safe to install? View the security audit →
🌀 OpenClaw Auto-Dream Your AI doesn't just remember. It dreams. A cognitive memory architecture that gives OpenClaw agents the ability to sleep, dream, and wake up smarter. Five memory layers. Importance scoring. Forgetting curves. Knowledge graphs. Health dashboards. Not file management — neuroscience. MyClaw.ai · ClawHub · OpenClaw 🌐 中文 · Français · Deutsch · Русский · 日本語 · Italia
| Stars | 548 |
| Forks | 26 |
| Language | HTML |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.7913211460233/100 |
| Open Issues | 4 |
| Last Updated | 2026-03-31 |
| Created | 2026-03-28 |
| Est. Tokens | ~33k |
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openclaw-auto-dream is Automatic memory consolidation for OpenClaw agents — like sleep for your AI. Powered by MyClaw.ai. It is categorized as a Agent Tool with 548 GitHub stars.
openclaw-auto-dream is primarily written in HTML. It covers topics such as ai-agent, auto-dream, llm.
You can find installation instructions and usage details in the openclaw-auto-dream GitHub repository at github.com/LeoYeAI/openclaw-auto-dream. The project has 548 stars and 26 forks, indicating an active community.
openclaw-auto-dream is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-auto-dream on Agent Skills Hub include openclaw-optimization-guide, lucid-memory, doc-to-lora. 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: