youtube-research

name

youtube-research

description

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"

YouTube Research

Research high-performing YouTube outlier videos, analyze top content with AI, and generate actionable reports.

Prerequisites

• TUBELAB_API_KEY environment variable. Get key from https://tubelab.net/settings/api
• GEMINI_API_KEY environment variable (for video analysis)
• google-genai and requests Python packages

Workflow

Step 1: Create Run Folder

mkdir -p youtube-research/$(date +%Y-%m-%d_%H%M%S)

Step 2: Get Channel ID

Read .claude/context/youtube-channel.md to get the channel ID.

Step 3: Fetch Channel Videos

python scripts/get_channel_videos.py CHANNEL_ID --format summary

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:

python .claude/skills/youtube-research/scripts/find_outliers.py \
  --keywords "keyword1" "keyword2" "keyword3" "keyword4" \
  --adjacent-keywords "adjacent1" "adjacent2" "adjacent3" "adjacent4" \
  --output-dir youtube-research/{run-folder} \
  --top 5

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).

Write the filtered videos to {RUN_FOLDER}/filtered-outliers.json:

{
  "outliers": [/* max 3 relevant videos */],
  "filter_reason": "Selected based on relevance to [user's niche]"
}

Step 7: Analyze Top Videos with AI

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \
  --input {RUN_FOLDER}/filtered-outliers.json \
  --output {RUN_FOLDER}/video-analysis.json \
  --platform youtube \
  --max-videos 3

Extracts from each video:

• Hook technique and replicable formula
• Content structure and sections
• Retention techniques
• CTA strategy

See the video-content-analyzer skill for full output schema and hook/format types.

Step 8: Generate Final Report

Read {RUN_FOLDER}/outliers.json and {RUN_FOLDER}/video-analysis.json, then generate {RUN_FOLDER}/report.md.

Report Structure:

# YouTube Research Report

Generated: {date}

## Top Performing Hooks

Ranked by engagement. Use these formulas for your content.

### Hook 1: {technique} - {channelTitle}
- **Video**: "{title}"
- **Opening**: "{opening_line}"
- **Why it works**: {attention_grab}
- **Replicable Formula**: {replicable_formula}
- **Views**: {viewCount} | **zScore**: {zScore}
- [Watch Video]({url})

[Repeat for each analyzed video]

## Content Structure Patterns

| Video | Format | Pacing | Key Retention Techniques |
|-------|--------|--------|--------------------------|
| {title} | {format} | {pacing} | {techniques} |

## CTA Strategies

| Video | CTA Type | CTA Text | Placement |
|-------|----------|----------|-----------|
| {title} | {type} | "{cta_text}" | {placement} |

## All Outliers

### Direct Niche
| Rank | Channel | Title | Views | zScore |
|------|---------|-------|-------|--------|
[List direct niche outliers]

### Adjacent Audience
| Rank | Channel | Title | Views | zScore |
|------|---------|-------|-------|--------|
[List adjacent outliers]

## Actionable Takeaways

[Synthesize patterns into 4-6 specific recommendations based on video analysis]

Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent.

Quick Reference

Full pipeline:

RUN_FOLDER="youtube-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && \
python .claude/skills/youtube-research/scripts/find_outliers.py \
  --keywords "k1" "k2" "k3" "k4" \
  --adjacent-keywords "a1" "a2" "a3" "a4" \
  --output-dir "$RUN_FOLDER" --top 5

Then filter outliers for niche relevance (max 3), run video analysis, and generate the report.

Script Reference

get_channel_videos.py

python .claude/skills/youtube-research/scripts/get_channel_videos.py CHANNEL_ID [--format json|summary]

Arg	Description
CHANNEL_ID	YouTube channel ID (24 chars)
--format	json (full data) or summary (for analysis)

find_outliers.py

python .claude/skills/youtube-research/scripts/find_outliers.py --keywords K1 K2 K3 K4 --adjacent-keywords A1 A2 A3 A4 --output-dir DIR [options]

Arg	Description
--keywords	Direct niche keywords (4 recommended)
--adjacent-keywords	Adjacent topic keywords (4 recommended)
--output-dir	Output directory (required)
--top	Videos per category (default: 5)
--days	Days back to search (default: 30)
--json	Also save raw JSON data

Output: outliers.json, report.md, thumbnails/, transcripts/

Scoring Algorithm

Videos ranked by: zScore × recency_boost

• zScore: How much video outperforms its channel average
• recency_boost: 1.0 for today, decays 5%/day (min 0.3×)