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 Xiangyue-Zhang · Agent Tool · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is auto-deep-researcher-24x7 safe to install? View the security audit →
Deep Researcher Agent 24/7 Autonomous Deep Learning Experiment Agent An AI agent that autonomously runs your deep learning experiments 24/7 while you sleep. English | 中文 | 日本語 | 한국어 <img src="ht
| Stars | 1,293 |
| Forks | 114 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 67.0325692397269/100 |
| Open Issues | 16 |
| Last Updated | 2026-06-03 |
| Created | 2026-04-08 |
| Platforms | claude-code, python |
| Est. Tokens | ~833k |
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auto-deep-researcher-24x7 is 🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.. It is categorized as a Agent Tool with 1.3k GitHub stars.
auto-deep-researcher-24x7 is primarily written in Python. It covers topics such as ai-agent, autonomous-agent, claude-code.
You can find installation instructions and usage details in the auto-deep-researcher-24x7 GitHub repository at github.com/Xiangyue-Zhang/auto-deep-researcher-24x7. The project has 1.3k stars and 114 forks, indicating an active community.
auto-deep-researcher-24x7 is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to auto-deep-researcher-24x7 on Agent Skills Hub include awesome-ai-tools, agent, gmickel-claude-marketplace. 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: