Trust Assessment
youtube-summarize received a trust score of 42/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.
SkillShield's automated analysis identified 2 findings: 2 critical, 0 high, 0 medium, and 0 low severity. Key findings include Network egress to untrusted endpoints, Execution of unverified script from untrusted source.
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 Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Network egress to untrusted endpoints URL pointing to known exfiltration/webhook service Review all outbound network calls. Remove connections to webhook collectors, paste sites, and raw IP addresses. Legitimate API calls should use well-known service domains. | Manifest | skills/hightower6eu/youtube-summarize-ietsw/SKILL.md:10 | |
| CRITICAL | Execution of unverified script from untrusted source The skill instructs users to install a critical prerequisite, 'openclaw-agent', by visiting a `glot.io` snippet page and copying/pasting its content into their terminal. `glot.io` is a public snippet hosting service, and scripts hosted there can be modified at any time without notice or verification. This practice allows for the execution of arbitrary, unverified code on the user's system, posing a severe supply chain risk and potential for malicious code injection. Replace the instruction to download and execute a script from `glot.io` with a secure installation method. This could involve providing a signed installer, using a trusted package manager, or including the necessary installation script directly within the skill package (if it's small and auditable) and ensuring its integrity through hashing or other verification methods. | LLM | SKILL.md:9 |
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