Youtube Channel Breakdown

YouTube Channel Breakdown Analysis

Comprehensive YouTube channel analysis pipeline for the Channel Breakdown Live Stream show. Runs the data collection pipeline, reads all collected data, and writes a single expert analysis document.

Usage

/youtube-channel-breakdown @channelhandle

Examples:

• /youtube-channel-breakdown @hubspotmarketing
• /youtube-channel-breakdown @aliabdaal
• /youtube-channel-breakdown @mkbhd

What This Skill Does

1. Data Collection - Runs the Python pipeline to fetch ALL videos from the channel
2. Expert Analysis - Reads all collected data and writes channel_analysis.md

The single deliverable is channel_analysis.md — a 4,000-5,000 word expert analysis written from the perspective of a YouTube strategist.

Instructions

When the user invokes this skill with a channel handle:

Step 1: Run the Research Pipeline

cd "/Users/nikhilbhansali/Library/CloudStorage/Dropbox/Claude Projects/Onewrk Digital Marketing and content research/07_Documentation/Channel_Breakdown_Live_Stream/scripts"
python3 channel_research_pipeline.py --channel "@channelhandle"

Wait for completion (can take 2-5 minutes for large channels). Note the output folder path printed at the end.

Step 2: Read All Data Files

Read these files from the output folder (research_data/[ChannelName]_[date]/):

1. channel_metrics.json - Basic channel stats
2. analysis_summary.json - Full analysis (content breakdown, top videos, upload consistency, title/SEO analysis, monetization, audience sentiment, growth trajectory, content formats, shorts patterns, live stream patterns, collaboration analysis, playlist strategy, description optimization, thumbnail patterns, creator background)
3. competitors.json - Discovered competitors with relevance scores and rankings
4. verification.json - Data validation results
5. top_comments.json - Top comments from top 3 videos
6. top_videos.json - Top 10 performing videos
7. research_brief.md - Markdown summary with content type tables
8. company_intelligence.json - Company news and context (if available)

Read ALL of these files. You need the complete data to write the analysis.

Step 3: Write the Expert Analysis

Write channel_analysis.md in the same output folder. Follow the template and guidelines below exactly.

Step 4: Report Completion

Tell the user:

• Output folder location
• Channel name, subscribers, total views, video count
• Data verification status
• A 2-3 sentence teaser of the most interesting finding
• Confirm channel_analysis.md has been written

---

channel_analysis.md Template (16 Sections)

The document MUST follow this exact structure. Adapt section content based on available data — skip subsections where data doesn't exist (e.g., skip "Live Streaming" section if channel has no live streams), but always include all sections that have supporting data.

Title Format

# [Channel Name]: A Strategic Analysis of [brief descriptor of what makes them notable]

Section I: The Channel at a Glance

A comprehensive metrics table. Pull ALL values from channel_metrics.json and analysis_summary.json:

| Metric | Value |
|--------|-------|
| Subscribers | [exact number, formatted with commas] |
| Total Views | [formatted] |
| Videos Published | [total] ([regular] regular, [shorts] shorts, [live] live) |
| Channel Created | [date] |
| Country | [country] |
| Average Views/Video | [calculated] |
| Engagement Rate | [from analysis] |
| Upload Frequency | [X videos per week/month] |
| Average Duration | [from analysis] |
| Estimated Monthly Views | [from analysis] |
| Views/Subscriber Ratio | [from analysis] |
| Channel Health | [score or assessment from analysis] |

Follow the table with 2-3 sentences contextualizing what these numbers mean relative to the niche.

Section II: Who Is [Creator/Brand] and Why Does This Channel Exist

• Creator/company background and credentials
• Origin story — what existed before the YouTube channel
• The founding insight or market gap they identified
• What problem this channel solves for its audience
• Target audience and their pain points

Source: analysis_summary.json (creator_background), company_intelligence.json, channel description from channel_metrics.json.

Section III: The Growth Story

This is the most important narrative section. Tell the story in phases:

• The Launch: First video details, early performance, initial strategy
• Phase 1: Early growth period, what formats they tried
• Phase 2: Strategic pivots, format evolution, what changed
• Phase 3: Format additions (Shorts, Live, etc.) and their impact
• Phase 4: Current state and business model
• Growth Trajectory Today: Recent performance vs historical (growing/stable/declining)
• Evergreen Content: Which old videos still get views and why

Source: analysis_summary.json (growthtrajectory, firstvideo, firsthitvideo, formatevolution, evergreencontent, recentvsolder).

Section IV: Content Strategy — The Machine Behind [X] Videos

• Content mix breakdown: regular videos vs shorts vs live streams (exact counts and percentages)
• Content format analysis: tutorials, vlogs, interviews, etc. (with performance by format)
• Duration distribution and sweet spots
• Upload cadence: consistency score, average days between uploads, gaps/breaks
• Publishing patterns: preferred days, times
• Content repurposing signals (if detectable)

Source: analysis_summary.json (contenttypebreakdown, contentformats, uploadconsistency, duration_analysis).

Section V: The Top 10 Videos — What Goes Viral

Include the top 10 table:

| # | Title | Views | Engagement | Type | Duration |
|---|-------|-------|------------|------|----------|

Then analyze:

• Viral threshold (what view count = viral for this channel, typically 5x average)
• Common patterns across top performers (format, topic, hook style, duration)
• What the #1 video reveals about the algorithm's preference for this channel
• Collaboration effects on top videos (if any)

Source: top_videos.json, analysis_summary.json (performancetiers, viralthreshold).

Section VI: Shorts Strategy Deep Dive

• Total shorts count, total shorts views, average views per short
• Engagement rate comparison: shorts vs long-form
• Hook patterns analysis (questions, numbers, caps, "this", etc.)
• Top 5 shorts with views
• Strategic role: are shorts feeding the main channel or standalone?

Source: analysis_summary.json (shortspatterns, contenttype_breakdown.shorts).

Skip this section if channel has fewer than 5 shorts.

Section VII: Live Streaming Analysis

• Total streams, average duration, average views
• Schedule patterns (day, time, frequency)
• Topic distribution across streams
• Top 5 streams with views
• Strategic function: community building, content generation, or both?

Source: analysis_summary.json (livestreampatterns, contenttypebreakdown.live_streams).

Skip this section if channel has fewer than 3 live streams.

Section VIII: Title, SEO, and Description Patterns

Title Analysis:

• Average title length (chars and words)
• Power word usage breakdown (urgency, curiosity, value, emotional, fear — with percentages)
• Title structure patterns (how-to, lists, questions, vs/comparisons — with percentages)
• Most effective title formulas based on top performers

Description Optimization:

• Optimization score (0-100)
• Timestamps/chapters usage rate
• Hashtag strategy
• CTA presence and types
• Link density
• Specific gaps and opportunities

Source: analysis_summary.json (titleanalysis, descriptionoptimization).

Section IX: Collaboration Strategy

• Total collaborations detected, collab rate percentage
• Performance comparison: collab videos vs solo (views, engagement)
• Most frequent collaborators (names if detectable)
• Strategic assessment: how collaborations serve growth

Source: analysis_summary.json (collaboration_analysis).

Skip if collab rate is 0%.

Section X: Playlist Architecture

• Total playlists, average playlist size
• Largest/most important playlists
• Series or course detection
• How playlists serve the content strategy (binge-watching, onboarding, etc.)

Source: analysis_summary.json (playlist_strategy).

Section XI: Audience and Comment Analysis

• Overall sentiment breakdown (positive/negative/neutral/questions)
• Top themes in comments (what viewers talk about most)
• Representative comments that reveal audience identity
• What comments reveal about the audience's relationship with the creator
• Creator's reply behavior and community culture

Source: top_comments.json, analysis_summary.json (audienceanalysis, commentsentiment).

Section XII: The Monetization Model

• Identified revenue streams (sponsorships, affiliates, products, memberships, courses, merch)
• Specific sponsors detected (names from analysis)
• Affiliate platforms detected
• Business funnel: how YouTube fits into broader business
• Revenue model assessment: diversified or dependent?

Source: analysis_summary.json (monetization_detection).

Section XIII: Competitive Position

Include comparison table:

| Channel | Subscribers | Total Views | Views/Video | Country |
|---------|------------|-------------|-------------|---------|

Then analyze:

• Where this channel ranks among competitors (by subs, views, views/video)
• Percentile position
• Views-per-subscriber ratio comparison (who's more efficient?)
• Gap to next level
• Unique positioning vs competitors

Source: competitors.json.

Section XIV: What This Channel Gets Right — Strategic Advantages

4-6 numbered strategic strengths. Each should be:

• A structural advantage, not just a surface observation
• Tied to specific data points
• Explained in terms of WHY it works (algorithm, audience psychology, business logic)

Section XV: What This Channel Could Improve

3-5 numbered opportunities. Each should be:

• Backed by specific data (e.g., "description optimization score is 35/100")
• Actionable and specific
• Prioritized by potential impact

Section XVI: Summary — The [Channel Name] Playbook

• 5-point distillation of what makes this channel work
• Long-term viability assessment
• One key question or prediction about the channel's future
• Closing statement on strategic positioning

---

Writing Guidelines

Follow these rules strictly:

1. Voice: Write as a YouTube strategist who understands platform mechanics, algorithm signals, and growth levers. NOT a generic content marketer or blogger.

1. No Data Fabrication: Every number must come from the JSON/CSV files you read. If a data point isn't available, say so or skip the subsection. NEVER invent statistics.

1. Narrative Interpretation: Don't just list metrics — explain what they mean. "Upload consistency score of 82 means this channel rarely misses a week, which signals reliability to the algorithm" is better than "Upload consistency: 82/100."

1. Specificity: Use exact numbers, reference actual video titles, quote real comments. Vague analysis is worthless.

1. Patterns Over Lists: Identify patterns and explain strategic choices. Don't enumerate facts.

1. Competitive Context: Position findings relative to competitors and niche norms.

1. Strategic Framing: Frame observations as strategic choices and their effects, not just descriptions.

1. Length: Target 4,000-5,000 words total. Sections I-II: ~500-800 words. Section III: ~800-1,000 words. Sections IV-XIII: ~200-400 words each. Sections XIV-XVI: ~300-500 words each.

1. Markdown Formatting: Use proper headers (##), tables, bold for emphasis, and blockquotes for standout insights.

1. Verification: Cross-check key numbers between files. If verification.json shows discrepancies, note them.

---

Output Location

All files saved to:

/Users/nikhilbhansali/Library/CloudStorage/Dropbox/Claude Projects/Onewrk Digital Marketing and content research/07_Documentation/Channel_Breakdown_Live_Stream/research_data/[ChannelName]_[YYYYMMDD]/

Files Generated

| File | Description | |------|-------------| | channel_metrics.json | Basic channel info and stats | | all_videos.csv | Every video with full metadata | | videos.csv | Regular videos only | | shorts.csv | Shorts only (<60s) | | live_streams.csv | Live streams only | | analysis_summary.json | Complete analysis with all metrics | | competitors.json | Competitor channels with stats and rankings | | top_videos.json | Top 10 performing videos | | top_comments.json | Top comments from top 3 videos | | verification.json | Data validation results | | research_brief.md | Markdown summary for live stream prep | | company_intelligence.json | Company news and context | | screenshot_urls.json | R2 CDN URLs for all images | | screenshots/ | Thumbnails and browser screenshots | | channel_analysis.md | Expert analysis document (written by Claude) |

Dependencies

Required:

• Python 3.11+
• google-api-python-client
• pandas

Optional (graceful fallback if missing):

• playwright (for browser screenshots)
• boto3 (for R2 uploads)

API Keys Required

• YouTube Data API v3 key in 02_SEO_Research/config/api_credentials.json
• Cloudflare R2 credentials in 06_Tools_APIs/cloudflare_r2/config/r2_config.json

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: nikhilbhansali
• Source: nikhilbhansali/youtube-data-skills
• License: MIT

Install and usage instructions live in the source repository linked above.