tomevault-io 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
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