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 kansoku-trade · Agent Tool · ★ 317
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is kansoku safe to install? View the security audit →
Kansoku 装在你 Mac 上的能查账的 AI 看盘搭子——它说的每句话你都能查:每个结论挂着完整证据链,归档之后不许改口,事后按命中率记分。 Kansoku(観測) 是一个 macOS 桌面应用:行情从你自己的长桥账户拉,指标全部本地实算,AI 用你自己配置的模型盯盘、答疑、改研究稿,结论落成本地文件。数据和 key 都不出你的机器。 下载安装 去 Releases 下载最新 版本的 (macOS · Apple Silicon),拖进「应用程序」即可。应用内置 Sparkle 自动更新(EdDSA 签名 + 增量包),装一次就不用再回来手动下载。 前置依赖:本机安装并登录 longbridge CLI(行情和账户数据都走它)。应用当前没有付费开发者签名,首次打开需要右键 →「打开」,详见 。 首次启动有引导:连上长桥数据,再选一个 AI 接入方式(本机 codex 登录态 / LobeHub Cloud / 自带 API key),也可以先跳过。 AI 能做什么 盘中自动点评,关掉图表还在巡检 —— 打开一只票,AI 结合多周期 K 线、形态标注和实时行情给出短线方向判断,以及 Bull/Base/Bear 三档情景推演(概率合计 100%,附触发条件)。开启跟进后,关掉图表它仍在后台按行情巡检,判断变了就通知你。右栏还带财报与宏观事件日历,页签在预测、环境、消息、复盘和 AI 点评之间切换。 追着分析问下去 —— 每份分析都能就地追问「凭什么」。面板浮在图上,可拖走、可缩放、可全屏。AI 会先读你的画线再结合实时行情回答;让它标关键价位时会直接画到图上(紫色虚线,悬停看说明),工具条可一键只清 AI 画的线。查了什么数据全程留痕,点开就是工具调用详情;答歪了随时停止,半截回答不会丢;空面板会先替你想好三个最该问的问题。已归档的预测是冻结记录,追问只解释、不改写。 研究库 AI 助手 —— 在应用里翻看和搜索本地的股票档案与研究日志,文档内直接对话提问。助手能
| Stars | 317 |
| Forks | 38 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 56.1133064310295/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-19 |
| Created | 2026-06-24 |
| Platforms | node |
| Est. Tokens | ~15k |
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kansoku is AI Stock Partner on Your Mac – Local Market Information, Multi-timeframe K-line, AI Intraday Comments and Follow-up Analysis. It is categorized as a Agent Tool with 317 GitHub stars.
kansoku is primarily written in TypeScript. It covers topics such as agent-skills, fastify, longbridge.
You can find installation instructions and usage details in the kansoku GitHub repository at github.com/kansoku-trade/kansoku. The project has 317 stars and 38 forks, indicating an active community.
The top alternatives to kansoku on Agent Skills Hub include kansoku, trade-skills, claudex. 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: