youtube-comment-analysis
Use when user requests YouTube comments. Run standalone for comment analysis or sequential with youtube-to-markdown for cross-analysis with video summary.
When & Why to Use This Skill
This Claude skill automates the extraction and deep analysis of YouTube comments to uncover actionable audience insights. It goes beyond basic summarization by filtering noise and using specialized sub-agents to identify common user failures, success patterns, and community debates. By transforming thousands of comments into a structured report, it provides a clear 'Voice of the Customer' for creators, marketers, and researchers.
Use Cases
- Case 1: Content Optimization - Identify specific points of confusion or 'Common Failures' in tutorial videos to create more effective follow-up content and improve viewer satisfaction.
- Case 2: Market & Competitor Research - Extract 'Alternatives Mentioned' and 'What Worked/Didn't' from competitor video comments to inform product positioning and feature development.
- Case 3: Community Sentiment Analysis - Analyze 'Points of Agreement/Debate' in interview or educational videos to understand audience perspectives and trending topics within a niche.
- Case 4: Knowledge Base Refinement - Use 'Corrections/Extensions' provided by expert commenters to update technical documentation or FAQs with real-world edge cases and solutions.
| name | youtube-comment-analysis |
|---|---|
| description | Use when user requests YouTube comments. Run standalone for comment analysis or sequential with youtube-to-markdown for cross-analysis with video summary. |
YouTube Comment Analysis
Execute all steps sequentially without asking for user approval. Use TodoWrite to track progress, update Todolist.
Step 0: Check for video summary
Check if <output_directory>/youtube - * ({video_id}).md exists. If found, use it for context in later steps.
Step 1: Extract comments
python3 ./extract_comments.py "<YOUTUBE_URL>" "<output_directory>"
Creates: youtube_{VIDEO_ID}name.txt, youtube{VIDEO_ID}_comments.md
Step 2: Prefilter comments
python3 ./prefilter_comments.py "<output_directory>/${BASE_NAME}_comments.md" "<output_directory>/${BASE_NAME}_comments_prefiltered.md"
Creates: youtube_{VIDEO_ID}_comments_prefiltered.md
Step 3: Extract Insightful Comments
task_tool:
- subagent_type: "general-purpose"
- model: "sonnet"
- prompt:
SUMMARY: "<output_directory>/youtube - * ({video_id}).md" if exists
INPUT: <output_directory>/${BASE_NAME}_comments_prefiltered.md
OUTPUT: <output_directory>/${BASE_NAME}_comment_insights.md
Detect video type from SUMMARY:
- TIPS: gear reviews, rankings, practical advice
- INTERVIEW: podcasts, conversations, Q&A
- EDUCATIONAL: concept explanations, analysis
- TUTORIAL: step-by-step instructions
Write to OUTPUT in format:
## Comment Insights ([2-7 word theme])
**Key Takeaway**: [One paragraph - ONLY if adds value beyond bullets]
[Include type-specific sections if found in comments:]
TUTORIAL:
- **Common Failures**: [what goes wrong, why, how to fix]
- **Success Patterns**: [what worked, time investment]
TIPS:
- **What Worked/Didn't**: [real-world validation]
- **Alternatives Mentioned**: [products, methods]
INTERVIEW:
- **Points of Agreement/Debate**: [where commenters align/clash]
- **Related Stories**: [personal experiences shared]
EDUCATIONAL:
- **Corrections/Extensions**: [where commenters add/fix content]
- **Debates**: [alternative viewpoints]
**[Additional themes as needed]**:
- [insight with **keyword highlights**]
Rules:
- Extract insights NOT already in summary
- Prioritize actionable over opinions
- Include commenter attribution only if expertise matters
ACTION REQUIRED: Use the Write tool NOW to save output to OUTPUT file.
Step 4: Review and tighten comment insights
task_tool:
- subagent_type: "general-purpose"
- model: "sonnet"
- prompt:
SUMMARY: "<output_directory>/youtube - * ({video_id}).md" if exists
INPUT: <output_directory>/${BASE_NAME}_comment_insights.md
OUTPUT: <output_directory>/${BASE_NAME}_comment_insights_tight.md
You are an adversarial copy editor. Your job is to ruthlessly cut fluff and enforce quality standards.
Rules:
- Remove insights already in summary file
- Cut filler, prefer lists over prose
- Keep only exceptional value-add insights
- Preserve type-specific sections (Common Failures, What Worked/Didn't, etc.)
ACTION REQUIRED: Use the Write tool NOW to save output to OUTPUT file.
Step 5: Finalize
python3 ./finalize_comments.py "${BASE_NAME}" "<output_directory>"
Output: youtube - {title} - comments ({video_id}).md
Use --debug flag to keep intermediate work files for inspection.