Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by context-machine-lab · Agent Tool · ★ 832
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Sleepless Agent A 24/7 AgentOS that works while you sleep Have Claude Code Pro but not using it at night? Transform it into an AgentOS that handles your ideas and tasks while you sleep. This is a 24/7 AI assistant daemon powered by Claude Code CLI and Python Agent SDK that processes both random thoughts and serious tasks via Slack with isolated workspaces. 📰 News [2025-10-26] 🎉 Initial release v0.1.0 - Full AgentOS with multi-agent workflow support [2025-10-25] 🚀 Added task auto-generation with configurable strategies [2025-10-24] 🔧 Integrated Git management with automatic PR creation [2025-10-23] 📊 Implemented isolated workspaces for parallel task execution [2025-10-22] 💡 Added Claude Code Python Agent SDK integration 🎬 Demo <img src="assets/sleepless.png" alt="Sleepless Ag
| Stars | 832 |
| Forks | 111 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.636220045715/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-29 |
| Created | 2025-10-21 |
| Platforms | claude-code, python |
| Est. Tokens | ~405k |
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sleepless-agent is 🤖 24/7 AI agent that maximizes Claude Code Pro usage via Slack. Auto-processes tasks, manages isolated workspaces, creates Git commits/PRs, and optimizes day/night usage thresholds.. It is categorized as a Agent Tool with 832 GitHub stars.
sleepless-agent is primarily written in Python. It covers topics such as ai-agent, anthropic, automation.
You can find installation instructions and usage details in the sleepless-agent GitHub repository at github.com/context-machine-lab/sleepless-agent. The project has 832 stars and 111 forks, indicating an active community.
sleepless-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to sleepless-agent on Agent Skills Hub include agentsys, meridian, linkedin-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: