Trust Assessment
todoist received a trust score of 95/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Potential Command Injection via Unsanitized User Input.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 14, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Potential Command Injection via Unsanitized User Input The skill's documentation demonstrates the use of the `td` command-line tool, which accepts various user-provided strings as arguments (e.g., task content in `td quick`, project names, labels, and filter expressions). If the LLM directly interpolates unsanitized user input into these command arguments without proper shell escaping, a malicious user could inject shell metacharacters (e.g., `;`, `|`, `&`, `$(...)`). This could lead to arbitrary command execution on the host system, allowing for data exfiltration, system modification, or other unauthorized actions. Ensure all user-provided input intended for `td` command arguments is rigorously sanitized and shell-escaped before constructing and executing the command. Implement a robust input validation and escaping mechanism to prevent shell metacharacters from being interpreted as commands. When using `subprocess` modules, prefer passing arguments as a list rather than a single string with `shell=True`. | LLM | SKILL.md:50 |
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