Youtube Notification Analysis

Analyze YouTube notifications for investment and trading insights. Use when user wants investment advice from YouTube, analyzing stock crypto or financial co...

This skill is internally inconsistent in ways that matter for safety: it tells the agent to download videos and run a local speech model, and—most importantly—to execute trades via a separate skill, but it declares no binaries, install steps, or credentials. Before installing or enabling it: 1) Confirm how trading will be authorized — never give live brokerage credentials without explicit, auditable confirmation and least-privilege controls; prefer a sandbox/test account. 2) Ensure yt-dlp and whisper-cpp (and the model file) come from trusted sources and understand disk/CPU impact. 3) Require explicit human confirmation before any trade execution and audit/logging to a secure location (not world-readable /tmp). 4) Verify the tiger-trade skill (or any trading integration) separately — check what credentials it needs and how it transmits orders. 5) If you don’t want automated trades, disallow autonomous invocation for this skill or remove the trade-execution steps from its instructions. If you want a lower-risk test, run the skill in a restricted environment with no trading credentials and monitor file/network activity.

Type: OpenClaw Skill Name: youtube-notification-analysis Version: 1.0.2 The skill is classified as suspicious due to its explicit capability to "execute trades" using a `tiger-trade` skill, which represents a high-risk financial operation. Additionally, the `SKILL.md` file contains shell commands using `yt-dlp` with unsanitized placeholders (`<video_url>`, `VIDEO_ID`), which introduces a shell injection vulnerability if the AI agent or user input is not properly sanitized. While there is no evidence of intentional malicious behavior like data exfiltration or backdoors, these combined factors present significant security risks.