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:

• 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

[Repeat for each analyzed video]

Content Structure Patterns

CTA Strategies

All Outliers

Direct Niche

Adjacent Audience

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×)