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 cablate · Claude Skill · ★ 375
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is banini-tracker safe to install? View the security audit →
banini-tracker AGPL-3.0 — 使用、修改或部署本專案，必須標明原作者 cablate、附上本 repo 連結，並以相同授權公開原始碼。網路服務部署亦同。 本專案於 2026/04/13 從 MIT 切換至 AGPL-3.0。此日期之前取得的版本仍適用 MIT 授權。 追蹤「股海冥燈」巴逆逆（8zz）的 Facebook 社群貼文，透過 AI 反指標分析、多平台即時推送（Telegram / Discord / LINE），並自動追蹤預測準確度。 辨識她提到的標的（個股、ETF、原物料） 反轉推導（她停損 → 可能反彈、她買入 → 可能下跌） 推導連鎖效應（油價跌 → 製造業利多 → 電子股受惠） 自動記錄預測，追蹤 5 個交易日的實際走勢 歷史回測 使用 Claude Opus 4.6 回溯分析 2024/04～2026/04 共 2,249 篇貼文，從中提取 345 筆明確的投資操作預測。 快速開始 四種使用方式，按推薦順序： Zeabur 一鍵部署（推薦） 部署後打開分配的網域，首次進入設定管理員帳密，然後在 Web UI 填入 API key 和通知管道。不需要手動設定環境變數。 Docker 部署後打開 進入設定頁面。也可以直接用 檔設定環境變數（見下方）。 npx 直接啟動 bash npx @cablate/banini-tracker serve npx
| Stars | 375 |
| Forks | 53 |
| Language | TypeScript |
| Category | Claude Skill |
| License | AGPL-3.0 |
| Quality Score | 63.9878841034913/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-04 |
| Created | 2026-04-09 |
| Platforms | claude-code, node |
| Est. Tokens | ~15k |
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banini-tracker is 巴逆逆（8zz）反指標追蹤器 — Facebook 抓取 + 影片轉錄 + AI 分析 + 多平台推送（Telegram / Discord / LINE）. It is categorized as a Claude Skill with 375 GitHub stars.
banini-tracker is primarily written in TypeScript. It covers topics such as apify, claude-code, discord.
You can find installation instructions and usage details in the banini-tracker GitHub repository at github.com/cablate/banini-tracker. The project has 375 stars and 53 forks, indicating an active community.
banini-tracker is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to banini-tracker on Agent Skills Hub include golembot, claude-telegram-supercharged, agent-deck. 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: