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 danilobatson · Agent Tool · ★ 312
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-trading-agent-gemini safe to install? View the security audit →
🤖 AI Trading Agent with Real-Time Social Sentiment Analysis Transform social media buzz into actionable trading signals using AI-powered analysis 🎯 What This Does This AI Trading Agent analyzes real-time social media sentiment from LunarCrush's API and generates intelligent trading signals using Google's Gemini AI. Watch as it processes social data through a 7-step background workflow with live progress tracking. ⚡ Live Demo: See It Live 🔥 Key Features 🚀 Real-time Progress Tracking - Watch AI analysis happen step-by-step with live updates 🧠 AI-Powered Signals - Google Gemini generates BUY/SELL/HOLD recommendations with confidence scores 📊 Social Sentiment
| Stars | 312 |
| Forks | 68 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 72.5720945469401/100 |
| Last Updated | 2026-01-05 |
| Created | 2025-06-12 |
| Platforms | browser, gemini, node |
| Est. Tokens | ~30k |
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ai-trading-agent-gemini is AI Trading Agent that transforms social media sentiment into actionable trading signals using LunarCrush analytics and Google Gemini AI. Features real-time progress tracking, background job processing. It is categorized as a Agent Tool with 312 GitHub stars.
ai-trading-agent-gemini is primarily written in TypeScript. It covers topics such as ai, cryptocurrency, google-gemini.
You can find installation instructions and usage details in the ai-trading-agent-gemini GitHub repository at github.com/danilobatson/ai-trading-agent-gemini. The project has 312 stars and 68 forks, indicating an active community.
The top alternatives to ai-trading-agent-gemini on Agent Skills Hub include LLM_trader, CloudBase-MCP, ai-trader. 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: