Agent skill · marketing growth · bradautomates
youtube-research
Research high-performing YouTube videos in a niche using TubeLab's outlier detection API. Identifies outlier videos, analyzes top 3 relevant videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending videos in a YouTube niche - Research competitor content - Discover viral video patterns - Generate content ideas based on what's working - Run YouTube research - Find outlier videos - Analyze hooks and content structure Triggers: "youtube research", "find outlier videos", "research YouTube trends", "what videos are performing well", "find content ideas for my channel", "youtube trends
Why this skill is useful
Adds specific commands and workflows for analyzing YouTube video performance that the AI wouldn't reliably generate on its own.
What it needs
Requires google-genai, requests installed locally. Requires gemini, tubelab account access. About 3k tokens when loaded. Last updated 2026-01-23. 197 stars on the source repository.
What this skill does
YouTube Research Research high-performing YouTube outlier videos, analyze top content with AI, and generate actionable reports. Prerequisites TUBELABAPIKEY environment variable. Get key from https://tubelab.net/settings/api GEMINIAPIKEY environment variable (for video analysis) google-genai and requests Python packages Workflow Step 1: Create Run Folder Step 2: Get Channel ID Read .claude/context/youtube-channel.md to get the channel ID. Step 3: Fetch Channel Videos This returns JSON with the channel's video titles and view counts. Step 4: Analyze Channel Analyze the channel data to extract: keywords: 4 search terms for the channel's direct niche adjacent-keywords: 4 search terms for topics the same audience watches audience: 2-3 profiles with objections, transformations, stakes formulas: Reusable title templates See references/channel-analysis-schema.md for the full schema and example output. Step 5: Search for Outliers Run the outlier search with both keyword sets: This runs two searches: Direct niche: keywords with 5K+ views threshold Adjacent audience: adjacent-keywords with 10K+ views threshold Output files: outliers.json - All outliers normalized for video analysis report.md - Basic markdown report thumbnails/.jpg - Video thumbnails transcripts/.txt - Video transcripts Step 6: Filter Relevant Videos for Analysis Read outliers.json and the user's niche from .claude/context/youtube-channel.md. CRITICAL: Select MAX 3 videos that are most relevant to the user's niche. Filter by: 1. Title relevance: Title contains keywords related to user's niche/topics 2. Transcript relevance: If transcript exists, check it mentions relevant topics 3. Direct niche priority: Prefer videos from direct keyword search over adjacent Skip videos that are clearly outside the user's content style (e.g., entertainment/vlogs when user does tutorials). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills bradautomates/youtube-research