YouTube Research
Workspace Context
Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.
Operating Contract
This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.
Three modes in one skill:
- Topic Research — competitive landscape, content gaps, strategic insights before planning a video
- Video Analysis — forensic deconstruction of transcripts to extract viral formulas and retention mechanics
- API Queries — direct YouTube Data API v3 access for search, stats, comments, and channel info
When to Use
- Researching a video topic before planning production
- Analyzing a competitor video to extract what makes it work
- Fetching channel stats, video metrics, or comments via the API
- Identifying content gaps and opportunities in a niche
YouTube Data API Setup
1. Get an API Key
- Go to Google Cloud Console → APIs & Services → Library
- Enable YouTube Data API v3
- Create Credentials → API Key
bashexport YOUTUBE_API_KEY="your-api-key-here"
Important: When piping curl output, wrap the command in
bash -c '...'to preserve env vars:bashbash -c 'curl -s "https://..." -H "..." | jq .'
2. Key API Commands
Search Videos:
bashbash -c 'curl -s "https://www.googleapis.com/youtube/v3/search?part=snippet&q=YOUR_QUERY&type=video&maxResults=10&order=viewCount&key=${YOUTUBE_API_KEY}"' | jq '.items[] | {videoId: .id.videoId, title: .snippet.title, channel: .snippet.channelTitle}'
Get Video Details (stats, duration):
bashbash -c 'curl -s "https://www.googleapis.com/youtube/v3/videos?part=snippet,statistics,contentDetails&id=VIDEO_ID&key=${YOUTUBE_API_KEY}"' | jq '.items[0] | {title: .snippet.title, views: .statistics.viewCount, likes: .statistics.likeCount, duration: .contentDetails.duration}'
Get Channel by Handle:
bashbash -c 'curl -s "https://www.googleapis.com/youtube/v3/channels?part=snippet,statistics&forHandle=@HANDLE&key=${YOUTUBE_API_KEY}"' | jq '.items[0] | {id: .id, title: .snippet.title, subscribers: .statistics.subscriberCount, videos: .statistics.videoCount}'
Get Video Comments:
bashbash -c 'curl -s "https://www.googleapis.com/youtube/v3/commentThreads?part=snippet&videoId=VIDEO_ID&maxResults=20&order=relevance&key=${YOUTUBE_API_KEY}"' | jq '.items[] | {author: .snippet.topLevelComment.snippet.authorDisplayName, text: .snippet.topLevelComment.snippet.textDisplay, likes: .snippet.topLevelComment.snippet.likeCount}'
Get Trending Videos:
bashbash -c 'curl -s "https://www.googleapis.com/youtube/v3/videos?part=snippet,statistics&chart=mostPopular®ionCode=US&maxResults=10&key=${YOUTUBE_API_KEY}"' | jq '.items[] | {title: .snippet.title, channel: .snippet.channelTitle, views: .statistics.viewCount}'
Quota: 10,000 units/day. Search = 100 units. Most others = 1 unit.
See YouTube Data API docs for full reference.
Mode 1: Topic Research
Conduct research before planning a new video. Focus on insights and big levers — not data dumping.
Workflow
Step 0: Create research file
Save all research to: ./youtube/episode/[episode_number]_[topic_short_name]/research.md
If it already exists, read it and continue from where it left off.
Step 1: Understand the topic
- What problem does this video solve?
- Why would someone click on it?
- What makes it relevant now?
Step 2: Research your own channel
Use the API to find related videos you've already published. Document:
- Related videos (title, video ID, URL, key metrics)
- What's already been covered and how to differentiate
Step 3: Competitor research
Search for 5–8 top videos on the topic. For each:
- Get video details (views, likes, duration)
- Note the title, angle, and what makes it successful
- Synthesize common patterns and approaches
Step 4: Content gap analysis
Document:
- What's saturated — 3–5 over-covered angles
- Gaps (Opportunities) — rated ⭐⭐⭐ high / ⭐⭐ medium / ⭐ low
- Recommended focus — specific angle + unique value proposition
Rating criteria:
- ⭐⭐⭐ High: Significant gap, strong demand, clear differentiation
- ⭐⭐ Medium: Moderate gap, some competition, good potential
- ⭐ Low: Minor gap, heavily competed
Research File Template
markdown# [Episode]: [Topic] - Research ## Episode Overview **Topic**: [Brief description] **Target Audience**: [Who this is for] **Goal**: [What viewers will learn/gain] ## YouTube Research ### Your Previous Videos [Related videos with metrics] ### Top Competing Videos [5-8 videos: title, channel, views, angle, what works] ### Key Insights [Patterns and findings synthesized] ## Content Gap Analysis ### What's Already Well-Covered [List] ### Content Gaps (Opportunities) [Rated list with ⭐ ratings] ### Recommended Focus [Specific angle and unique value proposition] ## Production Notes **Status**: Research Complete **Created**: [Date]
Parallel Research
If the host environment supports parallel research, split focused tasks such as competitor search, own-channel review, and comment mining. Otherwise, do them sequentially and synthesize findings after each section.
Pitfalls
- Data dumping — Limit to 5–8 competitors, synthesize patterns instead of listing every video
- Vague gaps — "Not much content on this" → identify the specific missing angle
- Long reports — Focus on insights and big levers
Next step: Use youtube-content skill to plan the video based on this research.
Mode 2: Video Analysis
Forensic deconstruction of video transcripts to extract viral formulas, hooks, and retention mechanics.
Getting the Transcript
Auto-fetch:
bashpython skills/youtube-research/scripts/fetch_transcript.py "YOUTUBE_URL_OR_VIDEO_ID"
Manual paste: YouTube's built-in transcript (click "..." → "Show transcript") or ytscribe.ai.
Analysis Framework
Approach the transcript like a crime scene — extract everything systematically. See reference/analysis-framework.md for the full checklist and templates.
Analyze these 11 dimensions:
- Hook Architecture — Primary hook (first 3–8s), hook type, secondary hooks, fill-in-blank templates
- Structural Blueprint — Content framework (PAS, Story-Lesson-CTA, List-Depth-Summary), beat map, pacing
- Retention Mechanics — Open loops, pattern interrupts, curiosity gaps, payoff points
- Emotional Engineering — Emotional arc, trigger words, identity hooks, Us vs. Them dynamics
- Storytelling Elements — Narrative framework, character positioning, conflict/stakes, specificity
- Linguistic Patterns — Power phrases, sentence rhythm, repetition, conversational triggers
- Algorithm Signals — Watch time optimizers, engagement bait, share/save triggers
- CTA Architecture — Primary CTA, soft CTAs, timing, value exchange
- Viral Coefficient — Shareability score (1–10), comment bait density, crossover potential
- Reusable Templates — Fill-in-blank opening hooks (3 variations), section templates, transition library
- Implementation Playbook — Top 10 steal-this elements, niche adaptation, A/B test suggestions
Before Analysis, Collect Context
- Your niche/topic
- Your content style (casual, educational, hype, etc.)
- Target platform and video length goal
Output Format
Structure output with all 11 sections. End with a Quick Reference Cheatsheet — one-page summary of all extracted patterns for rapid implementation.
Tools
- YouTube API:
bash -c 'curl ...'with$YOUTUBE_API_KEY - MCP (if available):
mcp__plugin_yt-content-strategist_youtube-analytics__search_videos,get_video_details,get_channel_details - Web:
WebSearchandWebFetchfor industry trends and context
