global-stock-data: 美股与全球市场数据工具包:官方源优先、零鉴权、标注合规级别

by simonlin1212 · Agent Tool · ★ 1.8k

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

Security audit verdict: SAFE · quality 68/100

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. Full audit →

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.

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Quick Facts

Stars1,834
Forks271
CategoryAgent Tool
LicenseApache-2.0
Quality Score68/100
Last Updated2026-11-02
Created2026-05-20
Platformsclaude-code
Est. Tokens~18k

About global-stock-data

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 .

Compatible Skills

These tools work well together with global-stock-data for enhanced workflows:

  • Equibles — semantic(0.38)+complementary+rare_topics+similar_pop+shared_platform (62%)
  • tickdb-unified-realtime-marketdata-api — semantic(0.39)+complementary+rare_topics+similar_pop (58%)
  • mcp_massive — semantic(0.47)+complementary+rare_topics+similar_pop (56%)
  • skills — semantic(0.30)+complementary+rare_topics+similar_pop+shared_platform (55%)
  • tradingview-mcp — semantic(0.27)+complementary+rare_topics+similar_pop+shared_platform (54%)

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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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