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 divanshusingh-ds · Agent Tool · ★ 2
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is TELREGRAM-BOT safe to install? View the security audit →
AI-powered Telegram assistant built with Python, Google Gemini, LangChain & LangGraph, featuring agentic tool calling, conversation memory, and modular AI architecture.
| Stars | 2 |
| Forks | 0 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 52.6757515579302/100 |
| Last Updated | 2026-08-29 |
| Created | 2026-08-23 |
| Platforms | gemini, python |
| Est. Tokens | ~1k |
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TELREGRAM-BOT is AI-powered Telegram assistant built with Python, Google Gemini, LangChain & LangGraph, featuring agentic tool calling, conversation memory, and modular AI architecture.. It is categorized as a Agent Tool with 2 GitHub stars.
TELREGRAM-BOT is primarily written in Python. It covers topics such as ai, ai-agents, artificial-intelligence.
You can find installation instructions and usage details in the TELREGRAM-BOT GitHub repository at github.com/divanshusingh-ds/TELREGRAM-BOT. The project has 2 stars and 0 forks, indicating an active community.
TELREGRAM-BOT is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to TELREGRAM-BOT on Agent Skills Hub include chainlit_langgraph, ha-claude, synkro. 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: