youtube-research

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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"

4installs

Sourcebradautomates/head-of-content

Added on2026-05-25

NPX Install

npx skill4agent add bradautomates/head-of-content youtube-research

SKILL.md Content

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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

bash

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

bash

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:

bash

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:

Title relevance: Title contains keywords related to user's niche/topics
Transcript relevance: If transcript exists, check it mentions relevant topics
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

:

json

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

Step 7: Analyze Top Videos with AI

bash

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:

markdown

# 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:

bash

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×)

youtube-research

NPX Install

Tags

SKILL.md Content

YouTube Research

Prerequisites

Workflow

Step 1: Create Run Folder

Step 2: Get Channel ID

Step 3: Fetch Channel Videos

Step 4: Analyze Channel

Step 5: Search for Outliers

Step 6: Filter Relevant Videos for Analysis

Step 7: Analyze Top Videos with AI

Step 8: Generate Final Report

Quick Reference

Script Reference

get_channel_videos.py

find_outliers.py

Scoring Algorithm