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 ArvinLovegood · Agent Tool · ★ 7.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is go-stock safe to install? View the security audit →
go-stock : 基于大语言模型的AI赋能股票分析工具 [//]: # ([](https://gitcode.com/ArvinLovegood/go-stock)) 🌟公众号 📈 交流群 [//]: # (- QQ交流群2：[点击链接加入群聊【go-stock交流群2】：892666282](https://qm.qq.com/q/5mYiy6Yxh0)) QQ交流群：点击链接加入群聊【go-stock交流群】：491605333(定期清理，随缘入群) ✨ 简介 本项目基于Wails和NaiveUI开发，结合AI大模型构建的股票分析工具。 目前已支持A股，港股，美股，未来计划加入基金，ETF等支持。 支持市场整体/个股情绪分析，K线技术指标分析等功能。 本项目仅供娱乐，不喜勿喷，AI分析股票结果仅供学习研究，投资有风险，请谨慎使用。 开发环境主要基于Windows10+，其他平台未测试或功能受限。 📃 使用手册 [go-stock使用手册](docs/go-stock使用手
| Stars | 7,812 |
| Forks | 1,369 |
| Language | Go |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 53.4656716190082/100 |
| Open Issues | 24 |
| Last Updated | 2026-10-11 |
| Created | 2024-12-17 |
| Platforms | go |
| Est. Tokens | ~18k |
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go-stock is 🦄🦄🦄AI赋能股票分析：AI加持的股票分析/选股工具。股票行情获取，AI热点资讯分析，AI资金/财务分析，涨跌报警推送。支持A股，港股，美股。支持市场整体/个股情绪分析，AI辅助选股等。数据全部保留在本地。支持DeepSeek，OpenAI， Ollama，LMStudio，AnythingLLM，硅基流动，火山方舟，阿里云百炼等平台或模型。. It is categorized as a Agent Tool with 7.8k GitHub stars.
go-stock is primarily written in Go. It covers topics such as ai-tools, deepseek, golang.
You can find installation instructions and usage details in the go-stock GitHub repository at github.com/ArvinLovegood/go-stock. The project has 7.8k stars and 1369 forks, indicating an active community.
go-stock is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to go-stock on Agent Skills Hub include plandex, codecompanion.nvim, deep-research. 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: