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 joeseesun · AI Tool · ★ 101
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AIGC_Telegram_Bot safe to install? View the security audit →
AIGCTelegramBot 编程新手用GPT4辅助开发的一个Telegram机器人，指定twitter ID，自动抓取推特消息并翻译成中文发给自己的Telegram账号。 使用方法 ① 创建Telegram机器人，获取Token 打开 https://t.me/botfather 输入 /start 按引导流程，先输入机器人名字，然后输入想要ID（必须以bot结尾），比如telegramrssbot 创建后会给Token，类似这种结构：5987500169:AAEBqLx7OWmK6ne9pIfHhrgMktDmqVcsSQ ② 获取自己的Telegram ID 打开 https://t.me/userinfobot 输入 \/start，拿到自己的ID，类似结构：1293676963 ③ 设置Token和Telegram ID 把Token和Telegram ID 填入env.txt文件，然后把env.txt改名为".env" ④ 安装依赖程序 ⑤ 运行程序 python main.py 想自定义关注人？ 修改 twitterlist.txt ，一个一个 twitter ID，逗号分割后面是名字，可自定义（非必须） 求帮优化改进 自己是产品经理，不怎么会代码。 以上都是GPT4帮写的，希望程序员大佬帮优化改进。
| Stars | 101 |
| Forks | 20 |
| Language | Python |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 45.9598033131137/100 |
| Open Issues | 1 |
| Last Updated | 2023-04-19 |
| Created | 2023-04-15 |
| Platforms | python |
| Est. Tokens | ~1k |
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AIGC_Telegram_Bot is 编程新手用GPT4辅助开发的一个Telegram机器人，指定twitter ID，自动抓取推特消息并翻译成中文发给自己的Telegram账号。. It is categorized as a AI Tool with 101 GitHub stars.
AIGC_Telegram_Bot is primarily written in Python.
You can find installation instructions and usage details in the AIGC_Telegram_Bot GitHub repository at github.com/joeseesun/AIGC_Telegram_Bot. The project has 101 stars and 20 forks, indicating an active community.
AIGC_Telegram_Bot is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to AIGC_Telegram_Bot on Agent Skills Hub include SciToolAgent, redesigned-pancake, claude-code-voice-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: