by Xiaoyuan-Liu · Claude Skill · ★ 52
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Claude Code skill：估计 A 股「国家队」(中央汇金) 宽基 ETF 持仓变动趋势 / Estimate China's national team (Central Huijin) broad-base ETF positioning
| Stars | 52 |
| Forks | 5 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 56.9879930012053/100 |
| Last Updated | 2026-07-20 |
| Created | 2026-05-29 |
| Platforms | claude-code, python |
| Est. Tokens | ~13k |
Looking for a national-team-position alternative? If you're comparing national-team-position with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
中国 A 股数据基础设施。42 个日更数据集：行情、基本面、资金面、公告事件、指数行业、宏观与风险。行级溯源、PIT 语义、复权与历史成分内置，MCP 原生。自托管，零注册、零 API Token
World-class creative director in a skill. Methodology-driven ideation (SIT, TRIZ, Lateral Thinking, bisociatio
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national-team-position is Claude Code skill：估计 A 股「国家队」(中央汇金) 宽基 ETF 持仓变动趋势 / Estimate China's national team (Central Huijin) broad-base ETF positioning. It is categorized as a Claude Skill with 52 GitHub stars.
national-team-position is primarily written in Python. It covers topics such as a-shares, agent-skill, akshare.
You can find installation instructions and usage details in the national-team-position GitHub repository at github.com/Xiaoyuan-Liu/national-team-position. The project has 52 stars and 5 forks, indicating an active community.
national-team-position is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to national-team-position on Agent Skills Hub include CNEquity, creative-director-skill, joinquant-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: