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 htlin222 · Agent Tool · ★ 94
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mini-claw safe to install? View the security audit →
Mini-Claw Lightweight Telegram bot for persistent AI conversations using Pi coding agent. A minimalist alternative to OpenClaw -- use your Claude Pro/Max or ChatGPT Plus subscription directly in Telegram, no API costs. Why Mini-Claw? You already pay for Claude Pro or ChatGPT Plus. Why pay again for API tokens? Mini-Claw lets you chat with your existing subscription through Telegram -- persistent sessions, file attachments, shell access, zero extra cost. Features Persistent Sessions - Conversations are saved and auto-compacted Workspace Navigation - Change directories with , run shell commands with Session Management - Archive, switch, and clean up old se
| Stars | 94 |
| Forks | 14 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 77.4919303916929/100 |
| Last Updated | 2026-03-28 |
| Created | 2026-02-01 |
| Platforms | claude-code, node |
| Est. Tokens | ~4k |
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mini-claw is 🦞 Minimalist alternative to OpenClaw — lightweight personal AI assistant with multi-platform messaging support. It is categorized as a Agent Tool with 94 GitHub stars.
mini-claw is primarily written in TypeScript. It covers topics such as ai-assistant, anthropic, chatbot.
You can find installation instructions and usage details in the mini-claw GitHub repository at github.com/htlin222/mini-claw. The project has 94 stars and 14 forks, indicating an active community.
mini-claw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mini-claw on Agent Skills Hub include openclaw-desktop, repopack-py, ha-claude. 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: