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"
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/bradautomates/head-of-content --skill youtube-research- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads youtube-research automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
YouTube Research
Research high-performing YouTube outlier videos, analyze top content with AI, and generate actionable reports.
Prerequisites
TUBELAB_API_KEYenvironment variable. Get key from https://tubelab.net/settings/apiGEMINI_API_KEYenvironment variable (for video analysis)google-genaiandrequestsPython 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 analysisreport.md- Basic markdown reportthumbnails/*.jpg- Video thumbnailstranscripts/*.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:
{
"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×)
FAQ
What does the youtube-research skill do?
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"
How do I install youtube-research?
Run npx skills add https://github.com/bradautomates/head-of-content --skill youtube-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/youtube-research/ (personal) or .claude/skills/youtube-research/ (project).
Which AI agents can use youtube-research?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is youtube-research safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains youtube-research?
It is published by bradautomates in bradautomates/head-of-content. It has 373 recorded installs.
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