vn_stock_advisor: Intelligent stock analysis tool utilizing CrewAI's Multi-AI-Agent system

by abcxyz91 · Agent Tool · ★ 62

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars62
Forks34
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score70/100
Open Issues1
Last Updated2025-06-14
Created2025-05-10
Platformspython
Est. Tokens~224k

About vn_stock_advisor

VN Stock Advisor (v.0.4.1) 🇻🇳 Tiếng Việt Giới thiệu VN Stock Advisor là một công cụ phân tích cổ phiếu thông minh sử dụng hệ thống Multi-AI-Agent của CrewAI. Công cụ cũng sử dụng trí tuệ nhân tạo được cung cấp bởi Google Gemini. Hệ thống cung cấp phân tích toàn diện về cổ phiếu, bao gồm tin tức, phân tích cơ bản và phân tích kỹ thuật và đưa ra khuyến nghị.

Compatible Skills

These tools work well together with vn_stock_advisor for enhanced workflows:

  • prism-insight — semantic(0.54)+complementary+same_lang+similar_pop+shared_platform (64%)
  • stock-mcp — semantic(0.42)+complementary+same_lang+similar_pop+shared_platform (60%)
  • tradingview-mcp — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • maverick-mcp — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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