# [Episode_Number]: [Topic] - Research

## Episode Overview
**Topic**: [Brief description]
**Target Audience**: [Who this is for]
**Goal**: [What viewers will learn/gain]

## Research Notes
### Key Concepts to Cover
[High-level list]

## YouTube Research
### Related Videos
**Your Previous Videos:** [Analysis]
**Top Competing Videos:** [5-8 videos with analysis]
**Key Insights:** [Patterns and findings]

## Content Gap Analysis
### What's Already Well-Covered: [List]
### Content Gaps (Opportunities): [Rated list]
### Recommended Focus: [Specific angle and value prop]

## Technical Implementation
[Only if applicable]

## Production Notes
**Episode Number**: [Number]
**Status**: Research Complete
**Created/Updated**: [Dates]

## Execution Guidelines

### Focus on Insights, Not Data
Execute research with these principles:
- Synthesize patterns from research
- Identify 3-5 key insights with supporting data
- Explain WHY approaches work
- Limit competitor research to 5-8 videos

### Prioritize Big Levers
Focus research on these impact areas in order:
1. Content Gaps (Unique value)
2. Competitor Patterns
3. Audience Needs
4. Technical Requirements

### Back Recommendations with Data
When documenting findings:
- ❌ "Make a video about AI agents"
- ✅ "Focus on AI agent memory systems (⭐⭐⭐ gap) - competitors get 50K+ views but don't cover persistent memory"

### Maintain Episode Continuity
During research:
- Reference previous episode research
- Check for topic overlap
- Identify opportunities to build on previous content

## Quality Checklist

Verify completion before finalizing research:
- [ ] Related videos and 5-8 competitors documented with analysis
- [ ] Content gaps identified with ⭐ ratings
- [ ] Research is concise yet comprehensive (not data dumping)
- [ ] All recommendations backed by data
- [ ] Unique value proposition clearly stated

## Tools to Use

Execute research using these tools:

**YouTube Analytics MCP**:
- `mcp__plugin_yt-content-strategist_youtube-analytics__search_videos` - Find videos by query
- `mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details` - Get video metrics
- `mcp__plugin_yt-content-strategist_youtube-analytics__get_channel_details` - Get channel info

**Web Research**: Use `web-search` and `web-fetch` for industry trends and context

**Filesystem**: Use `view` for channel context and previous research

## Common Pitfalls to Avoid

1. **Data Dumping**: Listing every video found without synthesis → Limit to 5-8 top videos, focus on patterns
2. **Vague Content Gaps**: "Not much content on this topic" → Identify specific angles missing
3. **Over-Researching Technical Details**: Deep implementation research → Keep high-level, focus on what to cover
4. **Long Reports**: 800+ line documents → Focus on insights and big levers

## Example Execution

**Scenario**: User requests research for video about "Building AI agents with memory"

Execute workflow:
1. Load channel context → Read CLAUDE.md, get channel details (1,500 subs, tech tutorial niche)
2. Find related videos → Search user's channel, find Episode 15 on personal assistants, viewers asked about memory
3. Competitor research → Search and analyze 8 top videos, identify they cover theory not implementation
4. Gap analysis → Document ⭐⭐⭐ opportunity for practical memory implementation
6. Save research → Write to `./youtube/18_ai_agents_with_memory/research.md`

**Result**: Comprehensive research document ready for review or to proceed to the planning phase.

**Next Step**: If the user has asked to plan the video, invoke the `youtube-plan-new-video` skill to generate title, thumbnail, and hook concepts based on this research.