By name: youtube-research description: Comprehensive YouTube research workflow with parallel agents for topic analysis, competitor research, SEO, and community insights. Use when user wants to research before creating YouTube content.
YouTube Research Workflow Skill
Comprehensive research system for YouTube video/series planning. Uses 5 specialized agents to gather, analyze, and synthesize research into actionable content strategy.
CRITICAL: Direct Agent Spawning
You (the main Claude agent) MUST spawn the gatherer and strategist agents directly.
Agents cannot spawn subagents, so there is no orchestrator. You handle the orchestration by:
- Creating folders
- Spawning 4 gatherers in parallel
- Waiting for completion
- Spawning the strategist
Architecture
/youtube research "Topic"
│
[Main Claude Agent handles orchestration]
│
├── @yt-topic-gatherer (Haiku) ─┐
├── @yt-competitor-gatherer (Haiku) ─┼─ Phase 1: Parallel
├── @yt-seo-gatherer (Haiku) ─┤
└── @yt-community-gatherer (Haiku) ─┘
│
▼
└── @yt-research-strategist (Opus) ─── Phase 2: Sequential
│
▼
research-pack.md + series-structure.md
Agent Summary
| Agent | Model | Purpose | Token Limit |
|---|---|---|---|
@yt-topic-gatherer |
Haiku | Subject matter, docs, features | 1,000 |
@yt-competitor-gatherer |
Haiku | YouTube videos, gaps | 1,000 |
@yt-seo-gatherer |
Haiku | Keywords, trends, titles | 800 |
@yt-community-gatherer |
Haiku | Reddit, forums, questions | 800 |
@yt-research-strategist |
Opus | Synthesis, series structure | N/A |
Total: 5 agents (4 gatherers + 1 strategist)
Workflow Phases
Phase 1: Parallel Gathering (~2-3 min)
Main Claude agent spawns 4 Haiku agents simultaneously (single message with 4 Task calls):
| Agent | Researches | Output Summary |
|---|---|---|
| Topic | Official docs, features, complexity | topic-summary.md |
| Competitor | Existing YouTube videos, gaps | competitor-summary.md |
| SEO | Keywords, trends, titles | seo-summary.md |
| Community | Reddit, forums, questions | community-summary.md |
Each agent:
- Gathers comprehensive raw data
- Saves raw data to
raw/folder - Creates condensed summary under token limit
- Saves summary to
summaries/folder
Phase 2: Strategic Synthesis (~3-5 min)
After all 4 gatherers complete, main Claude agent spawns Opus strategist:
- Reads all 4 summaries (~3,200 tokens)
- Uses sequential thinking for complex analysis
- Determines single video vs series
- Creates
research-pack.md - Creates
series-structure.md(if series)
Output Structure
03-YouTube/research/YYYY-MM-DD-[slug]/
├── research-pack.md # Strategic recommendations (always)
├── series-structure.md # Episode breakdown (if series)
├── summaries/ # Condensed inputs (~3,200 tokens)
│ ├── topic-summary.md
│ ├── competitor-summary.md
│ ├── seo-summary.md
│ └── community-summary.md
└── raw/ # Full research data
├── topic-raw.md
├── competitor-raw.md
├── seo-raw.md
└── community-raw.md
Research Pack Contents
| Section | Description |
|---|---|
| Executive Summary | 4-5 bullet points + recommendation |
| Topic Overview | Features, complexity, prerequisites |
| Competitor Landscape | Existing videos, gaps, differentiation |
| Target Audience | Who, what they know, what they want |
| SEO Strategy | Keywords, title suggestions, tags |
| Community Insights | Questions to answer, pain points |
| Content Recommendation | Single video or series + reasoning |
| Production Notes | Demo requirements, difficulty score |
| Next Steps | What to do after research |
Series Detection Criteria
Recommend Series When:
- Topic has 3+ distinct sub-topics
- Each sub-topic needs 10+ minutes coverage
- Clear learning progression exists
- Community has questions at multiple levels
- Too complex for single video
Recommend Single Video When:
- Topic is focused and contained
- Can cover in 15-30 minutes
- No natural breaking points
- Simple enough for one session
Tools Used by Agents
Topic Gatherer
- Perplexity (search, ask)
- Context7 (library docs)
- DeepWiki (GitHub projects)
- WebFetch
Competitor Gatherer
- Perplexity (search)
- WebSearch
- WebFetch
SEO Gatherer
- Perplexity (search)
- WebSearch
Community Gatherer
- Perplexity (search)
- WebSearch
- WebFetch
Strategist
- Sequential Thinking (complex analysis)
- Read, Write, Glob
Usage Examples
# Research for potential series
/youtube research "XCloud tutorial series"
# Research for single video
/youtube research "Docker networking basics"
# Research for advanced topic
/youtube research "Kubernetes security hardening"
# Research for beginner content
/youtube research "Self-hosting for beginners"
Timing Expectations
| Phase | Agents | Duration |
|---|---|---|
| Setup | Main agent | ~10 sec |
| Phase 1 | 4 Haiku (parallel) | ~2-3 min |
| Phase 2 | 1 Opus | ~3-5 min |
| Total | 5 agents | ~5-8 min |
Integration with YouTube Workflow
/youtube research "Docker Security"
↓
review: research-pack.md
↓
/youtube full "Docker Security" (or Part 1 if series)
↓
/youtube publish "Docker Security"
↓
/social video "docker-security"
Token Efficiency
Problem Solved: If 4 gatherers each return 10,000 tokens, strategist receives 40,000+ tokens - too much.
Solution: Each gatherer summarizes to strict token limits:
- Topic: 1,000 tokens max
- Competitor: 1,000 tokens max
- SEO: 800 tokens max
- Community: 800 tokens max
- Total to strategist: ~3,600 tokens
Raw data preserved in raw/ folder for reference if needed.
Quality Checklist
Before research is complete:
- Folders created (summaries/ and raw/)
- All 4 gatherers ran in parallel (single message)
- All 4 summary files exist
- Summaries are within token limits
- Raw data preserved
- research-pack.md is comprehensive
- Series detection reasoning is clear
- series-structure.md exists (if series)
- Next steps are actionable