This skill should be used when the user asks to "analyze YouTube content", "research YouTube trends", "extract hook patterns", "reverse-engineer successful videos", "compare YouTube channels", "find content gaps", "analyze competitor YouTube strategy", "research niche landscape", "extract video formulas", or needs data-driven YouTube content intelligence with deterministic structured output.
From youtube-toolkitnpx claudepluginhub nbkm8y5/claude-plugins --plugin youtube-toolkitThis skill uses the workspace's default tool permissions.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Executes pre-written implementation plans: critically reviews, follows bite-sized steps exactly, runs verifications, tracks progress with checkpoints, uses git worktrees, stops on blockers.
Guides idea refinement into designs: explores context, asks questions one-by-one, proposes approaches, presents sections for approval, writes/review specs before coding.
Comprehensive YouTube video analysis and pattern extraction for data-driven content strategy. Transform successful content research into actionable formulas and insights with deterministic structured output.
This skill analyzes YouTube content to extract proven patterns, hooks, structures, and formulas that drive engagement. Instead of guessing what works, you get data-driven insights from actual high-performing content in any niche.
The skill handles multiple input types — from direct video URLs to broad niche research — and outputs actionable content formulas in structured markdown format that can be post-processed into deterministic JSON.
Analyze specific high-performing videos by URL.
Input: List of YouTube URLs with optional view counts Use: Extract hook patterns, content structure, and engagement tactics from specific videos
Example prompt:
Analyze these mortgage education videos:
- https://youtube.com/watch?v=abc123 (2.3M views)
- https://youtube.com/watch?v=def456 (1.8M views)
Extract hook patterns, content structure, and engagement tactics.
Discover and analyze top performers for given keywords.
Input: Keywords/topics with performance criteria Use: Research top-performing content in a topic area
Example prompt:
Research the top 20 videos for "first time home buyer" with 500K+ views from the last 12 months. Focus on hook patterns and educational frameworks.
Extract successful patterns from specific creators.
Input: Channel name/URL with analysis scope Use: Competitive intelligence on individual channels
Example prompt:
Analyze Graham Stephan's top 15 performing videos from 2025. What content formulas and hook patterns drive his success?
Comprehensive market mapping for a topic area.
Input: Broad topic area with market parameters Use: Identify trends, gaps, top creators, and opportunities
Example prompt:
Map the real estate education landscape on YouTube. Find top creators, trending formats, content gaps, and emerging opportunities for Spanish-language content.
Extract patterns for specific video formats.
Input: Content type with performance benchmarks Use: Optimize for specific format (Shorts, longform, livestream, etc.)
Example prompt:
Analyze YouTube Shorts about credit scores with 1M+ views. Extract hook formulas, visual patterns, and engagement tactics specific to vertical format.
Compare approaches across language markets.
Input: Topic with language/region specifications Use: Cultural adaptation insights and untapped market identification
Example prompt:
Compare mortgage education content between English and Spanish YouTube. Analyze top performers in each language for cultural adaptation insights.
| Hook Type | Pattern | Psychology |
|---|---|---|
| Question | "What if I told you..." | Engages curiosity, creates knowledge gap |
| Claim | "I made $10K in 30 days..." | Authority signal, desire activation |
| Story | "This mistake cost me everything..." | Empathy, narrative engagement |
| Statistic | "97% of people don't know this..." | Authority, exclusivity, FOMO |
| Pattern Interrupt | Unexpected visuals or statements | Breaks scroll behavior, demands attention |
| Controversy | "Everyone is wrong about..." | Contrarian positioning, debate engagement |
| Transformation | "Before vs After" opening | Visual proof, aspiration activation |
CRITICAL: Output must follow this exact section structure for post-processing scripts. The Python post-processor (format_youtube_json.py) parses these exact ## SECTION HEADER markers to extract structured data for JSON export.
## HOOK PATTERNS
### Pattern 1: [Hook Type Name]
**Success Rate**: [X% of analyzed videos using this pattern]
**Pattern**: [Structural description of the hook]
**Example**: "[Actual hook from analyzed content]"
**Psychology**: [Why this hook works — cognitive trigger, emotional driver]
**Adaptation**: [How to apply this pattern to your own content]
### Pattern 2: [Hook Type Name]
**Success Rate**: [X%]
**Pattern**: [Description]
**Example**: "[Example]"
**Psychology**: [Analysis]
**Adaptation**: [Application guide]
### Pattern 3: [Hook Type Name]
**Success Rate**: [X%]
**Pattern**: [Description]
**Example**: "[Example]"
**Psychology**: [Analysis]
**Adaptation**: [Application guide]
[Additional patterns as discovered — minimum 3, maximum 8]
---
## CONTENT STRUCTURE
### Formula 1: [Formula Name]
**Duration**: [Average video length using this formula]
**Win Rate**: [% of high-performing videos using this structure]
**Structure**:
1. [0:00-X:XX] [Phase name] — [Description]
2. [X:XX-X:XX] [Phase name] — [Description]
3. [X:XX-X:XX] [Phase name] — [Description]
4. [X:XX-X:XX] [Phase name] — [Description]
5. [X:XX-X:XX] [Phase name] — [Description]
**Retention Impact**: [How this structure affects watch time]
**Best For**: [Content types, audience segments, niches]
### Formula 2: [Formula Name]
**Duration**: [Length]
**Win Rate**: [%]
**Structure**:
[Same format]
**Retention Impact**: [Analysis]
**Best For**: [Application]
[Additional formulas — minimum 1, maximum 4]
---
## MARKET OPPORTUNITIES
### Gap 1: [Underserved Topic or Angle]
**Search Volume**: [Estimated monthly searches or High/Medium/Low]
**Competition**: [Low/Medium/High with reasoning]
**Opportunity Score**: [1-10 rating with justification]
**Why Underserved**: [Analysis of why this gap exists]
**Recommended Approach**: [How to fill this gap effectively]
### Gap 2: [Format Opportunity]
**Search Volume**: [Estimate]
**Competition**: [Level]
**Opportunity Score**: [Rating]
**Why Underserved**: [Analysis]
**Recommended Approach**: [Strategy]
### Gap 3: [Audience Segment]
**Search Volume**: [Estimate]
**Competition**: [Level]
**Opportunity Score**: [Rating]
**Why Underserved**: [Analysis]
**Recommended Approach**: [Strategy]
[Additional gaps — minimum 2, maximum 6]
---
## RECOMMENDATIONS
### Immediate Actions
1. **[Action Name]**: [Specific, implementable recommendation with reasoning]
2. **[Action Name]**: [Recommendation]
3. **[Action Name]**: [Recommendation]
### A/B Tests
1. **[Test Name]**: [What to test, expected outcome, success metric]
2. **[Test Name]**: [Test description]
3. **[Test Name]**: [Test description]
### Strategic Priorities
1. **[Priority]**: [Long-term strategic recommendation with timeline]
2. **[Priority]**: [Recommendation]
---
## IMPLEMENTATION CHECKLIST
- [ ] [Specific action item 1]
- [ ] [Specific action item 2]
- [ ] [Specific action item 3]
- [ ] [Specific action item 4]
- [ ] [Specific action item 5]
- [ ] [Specific action item 6]
- [ ] [Specific action item 7]
- [ ] [Specific action item 8]
[8-15 checklist items, ordered by priority/sequence]
The --type flag controls which sections are generated:
| Type | Sections Generated |
|---|---|
hooks | HOOK PATTERNS only |
structure | CONTENT STRUCTURE only |
market | MARKET OPPORTUNITIES only |
full (default) | All 5 sections |
When --type is not full, omit the sections not requested but maintain the same ## SECTION HEADER format for the included sections.
This skill's output feeds into the n8n automation pipeline:
n8n SSH Node → claude -p "/youtube-toolkit:analyze-content --input '...' --type full --format json"
└─ format_youtube_json.py --skill intelligence → JSON
├─ Content calendar (WordPress/Notion)
├─ Strategy dashboards
└─ youtube-metadata skill (informed generation)
Intelligence output directly informs youtube-metadata skill usage:
Research successful real estate education content and create a 6-month content calendar based on proven topics and formats.
Analyze my top 3 competitors and identify content opportunities they're missing.
Extract the most effective opening hooks for mortgage education videos and adapt them for my Spanish-speaking audience.
Compare YouTube Shorts vs long-form performance in my niche and recommend optimal content mix.
Identify emerging trends in financial education content and suggest topics to cover before they saturate.
Analyze English-language mortgage content success patterns and recommend adaptations for Hispanic audiences.