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 brainstormity · AI Tool · ★ 100
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Jev-X-Sentiment-Analysis safe to install? View the security audit →
Jev X Sentiment Analysis An on-demand crypto market intelligence and decision-support terminal powered by TypeSafe AI's System One model (Jev). The tool allows you to search any cryptocurrency (such as BTC, SOL, or ETH), select how many tweets you want to analyze (from 50 up to 1,000 tweets), and receive an instant, data-backed trading decision (Buy, Sell, Hold, or Take Profit) based on real-time market data, perpetuals funding rates, and social sentiment. The platform does not execute trades automatically. It generates a clear decision card with calculated entry ranges, stop losses, and target levels so you can review the reasoning and execute manually on whichever exchange or DEX you prefer. How Tweets Are Analyzed When you request sentiment analysis across 100 to 1,000 tweets, dumping hundreds of raw tweets into an LLM would exceed token budgets and introduce latency. Instead, Jev X Sentiment Analysis uses an intelligent two-tier pipeline: text User Search (e.g.
| Stars | 100 |
| Forks | 11 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 60.7883191880848/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-19 |
| Created | 2026-09-19 |
| Platforms | python |
| Est. Tokens | ~15k |
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Jev-X-Sentiment-Analysis is an open-source ai tool by brainstormity with 100 GitHub stars.
Jev-X-Sentiment-Analysis is primarily written in Python.
You can find installation instructions and usage details in the Jev-X-Sentiment-Analysis GitHub repository at github.com/brainstormity/Jev-X-Sentiment-Analysis. The project has 100 stars and 11 forks, indicating an active community.
The top alternatives to Jev-X-Sentiment-Analysis on Agent Skills Hub include SciToolAgent, redesigned-pancake, claude-code-voice-skill. 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: