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 autumnseasonism · Agent Tool · ★ 138
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is lark-todo safe to install? View the security audit →
lark-todo 飞书全平台待办行动扫描器 · 支持多企业账号 一句话触发全平台扫描：自动采集 IM 消息、会议纪要、日程、文档评论、待办审批、我发起的审批、邮件、已有任务，智能排列优先级，支持直接处理或创建飞书任务。多个企业账号并行扫描，跨企业合并排序。 English 它解决什么问题 每天打开飞书，消息 999+、审批红点、日程提醒、文档评论……分散在各个角落，翻一圈下来 20 分钟过去了，还不确定有没有漏掉什么。在多个公司都有飞书账号？那就翻好几遍。 所以我做了 lark-todo：一句话下去，所有企业账号 × 8 个数据源并行扫描，AI 按紧急程度跨企业合并排序，能当场处理的直接处理，不能的创建任务。 一个账号还是多个，都只需要说一句话。 🧭 Before / After 💬 一句话怎么用 AI Agent 自动完成： 采集 — 并行扫描 IM 消息、会议纪要、日程、文档评论、待办审批、我发起的审批、已有任务、
| Stars | 138 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 59.1308835516275/100 |
| Last Updated | 2026-04-21 |
| Created | 2026-04-15 |
| Platforms | python |
| Est. Tokens | ~9k |
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lark-todo is AI Agent Skill - 扫描飞书全平台待办事项，智能排序，直接处理或创建任务. It is categorized as a Agent Tool with 138 GitHub stars.
lark-todo is primarily written in Python.
You can find installation instructions and usage details in the lark-todo GitHub repository at github.com/autumnseasonism/lark-todo. The project has 138 stars and 17 forks, indicating an active community.
lark-todo is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to lark-todo on Agent Skills Hub include claude-plugins, tutor-skills, repren. 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: