youtube-research

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:

1. Topic Research — competitive landscape, content gaps, strategic insights before planning a video
2. Video Analysis — forensic deconstruction of transcripts to extract viral formulas and retention mechanics
3. API Queries — direct YouTube Data API v3 access for search, stats, comments, and channel info

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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

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YouTube Data API Setup

1. Get an API Key

1. Go to Google Cloud Console → APIs & Services → Library
2. Enable YouTube Data API v3
3. Create Credentials → API Key

export YOUTUBE_API_KEY="your-api-key-here"

Important: When piping curl output, wrap the command in bash -c '...' to preserve env vars:

bash -c 'curl -s "https://..." -H "..." | jq .'

2. Key API Commands

Search Videos:

bash -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):

bash -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:

bash -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:

bash -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:

bash -c 'curl -s "https://www.googleapis.com/youtube/v3/videos?part=snippet,statistics&chart=mostPopular&regionCode=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.

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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

# [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.

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Mode 2: Video Analysis

Forensic deconstruction of video transcripts to extract viral formulas, hooks, and retention mechanics.

Getting the Transcript

Auto-fetch:

python 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:

1. Hook Architecture — Primary hook (first 3–8s), hook type, secondary hooks, fill-in-blank templates
2. Structural Blueprint — Content framework (PAS, Story-Lesson-CTA, List-Depth-Summary), beat map, pacing
3. Retention Mechanics — Open loops, pattern interrupts, curiosity gaps, payoff points
4. Emotional Engineering — Emotional arc, trigger words, identity hooks, Us vs. Them dynamics
5. Storytelling Elements — Narrative framework, character positioning, conflict/stakes, specificity
6. Linguistic Patterns — Power phrases, sentence rhythm, repetition, conversational triggers
7. Algorithm Signals — Watch time optimizers, engagement bait, share/save triggers
8. CTA Architecture — Primary CTA, soft CTAs, timing, value exchange
9. Viral Coefficient — Shareability score (1–10), comment bait density, crossover potential
10. Reusable Templates — Fill-in-blank opening hooks (3 variations), section templates, transition library
11. 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.

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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: WebSearch and WebFetch for industry trends and context