by FTShare-Lab · Agent Tool · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
| Stars | 50 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-19 |
| Created | 2026-06-28 |
| Platforms | python |
| Est. Tokens | ~14k |
FTShare Skills 中文 | English 是 ftshare 开源生态中的 Agent Skill 仓库，用于沉淀金融数据 Skill 和投研业务 Skill。 本仓库面向 Claude Code、Codex、OpenClaw 等 Agent 运行时，目标是把 FTShare 的金融数据能力和投研业务流程封装成 Agent 可理解、可调用、可复用的 Skill，让 Agent 能从“调用数据”进一步走向“完成投研任务”。 仓库定位 不是单一数据接口仓库，也不是 Python SDK。 它主要承载两类能力： 数据级 Skill 封装 FTShare 行情、财务、宏观、指数、ETF、基金、港美股、新闻等数据接口，让 Agent 能稳定获取结构化金融数据。 业务级 Skill 基于数据能力进一步组织个股分析、财报解读、行业分析、盘后复盘、持仓分析等投研工作流，让 Agent...
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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: