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 simonlin1212 · Agent Tool · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is global-stock-data safe to install? View the security audit →
global-stock-data · US & HK Market Data for AI Coding Assistants Architecture · Compliance · Quick Start · Endpoints · Data Sources · FAQ · 🇨🇳 中文文档 Full-stack market data for US & HK equities — 13 layers · 30+ endpoints · 11 sources · zero-auth · only . Official-source-first (V2.0): CBOE options with full Greeks + 0DTE flow, FINRA market-wide short volume, SEC EDGAR filing stream + a free market-wide screener, Treasury / CFTC / earnings calendar. Every source is labeled with its compliance tier, terms quoted verbatim — "official" does not mean "free to use". A single self-contained Skill file that turns raw US/HK stock data — scattered acro
| Stars | 1,608 |
| Forks | 244 |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 69.1241446950447/100 |
| Last Updated | 2026-09-16 |
| Created | 2026-05-20 |
| Platforms | claude-code |
| Est. Tokens | ~18k |
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global-stock-data is US stock market data for AI coding assistants — zero-auth, official sources. CBOE options with full Greeks + 0DTE flow, FINRA market-wide short volume, SEC EDGAR filing stream, and a free market-wide . It is categorized as a Agent Tool with 1.6k GitHub stars.
You can find installation instructions and usage details in the global-stock-data GitHub repository at github.com/simonlin1212/global-stock-data. The project has 1.6k stars and 244 forks, indicating an active community.
global-stock-data is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to global-stock-data on Agent Skills Hub include code-graph-rag, linkedin-mcp-server, google_workspace_mcp. 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: