wbh604/UZI-Skill

冰冷的钱就这样流进我温暖的口袋-游资(UZI)Skills — 让我们欢迎,股海贼王!66位投资大佬帮你看盘 · 22维数据 × 180条量化规则 × 17种机构分析方法 · A股/港股/美股

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wbh604/UZI-Skill has 7k+ stars and 1k+ forks on GitHub, is written in Python, and was last pushed on Oct 17, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 21 minutes ago
Added to GitGenius on September 1st, 2026
Created on April 16th, 2026
Open Issues & Pull Requests: 0 (+0)
GitHub issues: Enabled
Number of forks: 1,014
Total Stargazers: 7,266 (+0)
Total Subscribers: 30 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 11.5 hours
Mean response time: 2.3 days
90th percentile: 7.9 days
Tracked items: 43

How this project is maintained

About 12% of issues opened in the past year have never received a reply. 86% of issues opened in the past year have been closed, leaving a working backlog. Three people close 100% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 0
New in 7 days: 0
Closed in 7 days: 7
Avg open age: N/A days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 7
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (13)
  • enhancement (9)

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Detailed Description

UZI-Skill is a stock analysis engine that provides deep fundamental and technical analysis for individual securities across Chinese, Hong Kong, and US markets.

The tool addresses the challenge of evaluating individual stocks by synthesizing analysis from multiple perspectives. It operates by aggregating insights from a panel of 66 investment strategists representing 9 different trading schools, analyzing 22 dimensions of data, and applying 22 institutional-grade analytical methods. The system generates detailed reports that examine stock behavior through varied analytical lenses, from value investing to technical analysis to quantitative rule-based approaches.

Developers and investors should adopt this tool if they need comprehensive single-stock analysis rather than fund or portfolio evaluation. The project explicitly positions itself as complementary to a separate tool focused on active fund manager behavior analysis. The tool integrates directly into multiple AI agent platforms including Claude Code, Cursor, Gemini CLI, and others, as well as functioning as a standalone command-line utility. Installation is straightforward across these environments, with platform-specific setup instructions provided.

Development activity shows consistent refinement of the user-facing analysis output, with recent updates focused on frontend display fixes and visual improvements. The project maintains detailed documentation of its evolution through a changelog and provides contributor guidelines. The tool supports multiple integration pathways and maintains compatibility across different agent frameworks, indicating ongoing attention to accessibility and usability across diverse deployment scenarios.