YT Shorts Niche Research

Find viral YouTube Shorts channels that started recently and are doing really well. Use when Abdullah asks to find shorts niches, find channels, research You...

What to consider before installing: - The skill provided no code or install steps but expects a local script at C:\Users\sarum\.openclaw\workspace\youtube-research.js and a Node/browser environment. Ask the author for the actual script and installation instructions before running anything. - The instructions require writing files (youtube-research-YYYY-MM-DD.json, HEARTBEAT.md), repeatedly running background jobs, and sending data to Telegram weekly — but no Telegram token or other credentials are declared. Do not provide any secret tokens until you verify exactly how they are used. - The SKILL.md tells the main session not to read result files and to use a subagent instead — this is unusual and could be an attempt to bypass review or auditing. Ask why this design is necessary and request to inspect the output files and the script logic. - If you consider using it: get the source code, review the script for what it scrapes/sends, confirm it respects YouTube terms of service, and ensure any scheduled/unprompted actions are under your control (or remove them). - If you do not trust the author or cannot review the script, do not run it and prefer a skill that includes its code, declares necessary binaries/credentials, and does not require hidden background scheduling.

Type: OpenClaw Skill Name: youtube-shorts-niche-research Version: 1.0.0 The skill is classified as suspicious due to instructions in SKILL.md that command the agent to perform autonomous, 'unprompted' data transmission to a third party (Abdullah via Telegram) and maintain a 'Weekly Schedule' for background execution. It relies on hardcoded absolute file paths (C:\Users\sarum\...) and mandates persistent looping until strict criteria are met, which could lead to resource exhaustion. While the stated goal is YouTube research, the inclusion of agentic persistence and external messaging instructions without explicit user triggers represents a high-risk pattern for unauthorized automated activity.