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 todo-for-ai · MCP Server · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is todo-for-ai safe to install? View the security audit →
Todo for AI 中文版本 | English 🚀 A powerful task management system specifically designed for AI assistants, supporting intelligent project management, automated task tracking, and seamless team collaboration through MCP (Model Context Protocol) integration. 🚀 Try it now: Visit https://todo4ai.org/ to experience our product! 🤖 Why Todo for AI? Todo for AI bridges the gap between AI assistants and human productivi
| Stars | 1,183 |
| Forks | 104 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 77.2381800480509/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-19 |
| Created | 2025-07-28 |
| Platforms | docker, mcp, python |
| Est. Tokens | ~17k |
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todo-for-ai is 🤖 A comprehensive task management system specifically designed for AI assistants. Supports project management, task tracking, team collaboration, and seamless AI integration through MCP (Model Contex. It is categorized as a MCP Server with 1.2k GitHub stars.
todo-for-ai is primarily written in Python. It covers topics such as ai-coding-tools, cursor, kiro.
You can find installation instructions and usage details in the todo-for-ai GitHub repository at github.com/todo-for-ai/todo-for-ai. The project has 1.2k stars and 104 forks, indicating an active community.
todo-for-ai is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to todo-for-ai on Agent Skills Hub include any-auto-register, system-prompts-and-models-of-ai-tools-chinese, system-prompts-and-models-of-ai-tools-chinese. 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: