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 qrak · LLM Plugin · ★ 129
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LLM_trader safe to install? View the security audit →
🤖 SEMANTIC SIGNAL LLM (LLM Trader) An autonomous AI trading agent that reads charts, remembers outcomes, and sharpens its strategy in real time. []() []() []() 📊 Live Dashboard — Watch the neural trading brain in action 📖 Read the Full Story (Medium) 💬 Join the Discord ⚠️ Research Edition. Runs in paper-trading mode only. Real exchange order execution is not implemented in this public branch. Quick Start Detailed setup for Windows, Linux, macOS → Platform-specific scripts live in — they handle venv creation, dependency install, and startup: | scripts/st
| Stars | 129 |
| Forks | 45 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 67.5812015483884/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-25 |
| Created | 2025-02-28 |
| Platforms | cli, gemini, python |
| Est. Tokens | ~15k |
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LLM_trader is LLM-powered Crypto Trading Framework with Vision AI chart analysis, real-time Neural Engine, and a live monitoring dashboard at semanticsignal.qrak.org. Features memory-augmented reasoning and profess. It is categorized as a LLM Plugin with 129 GitHub stars.
LLM_trader is primarily written in Python. It covers topics such as ai, algorithmic-trading, algorythmic-trading.
You can find installation instructions and usage details in the LLM_trader GitHub repository at github.com/qrak/LLM_trader. The project has 129 stars and 45 forks, indicating an active community.
LLM_trader is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to LLM_trader on Agent Skills Hub include runprompt, SimplerLLM, ccproxy. 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: