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Zhongwei Li
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skills/youtube-research-video-topic/SKILL.md
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name: youtube-research-video-topic
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description: Conduct pure research for YouTube video topics by analyzing competitors, identifying content gaps, and documenting strategic insights. Use when you need to research a video topic before planning. Produces concise, insight-focused research documents that identify the biggest opportunities for video performance.
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---
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# YouTube Video Topic Research
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## Overview
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This skill conducts pure research for YouTube video topics. Execute all steps to produce actionable insights that identify content gaps and analyze competitors. This skill focuses ONLY on research - it does not generate titles, thumbnails, or hooks.
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**Core Principle**: Focus on insights and big levers, not data dumping. Research should be comprehensive yet concise, backed by data, and designed to inform strategic decisions.
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## When to Use
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Use this skill when:
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- You need to research a video topic before planning production
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- The user asks to research a video idea or topic
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- You want to understand the competitive landscape
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- You need to identify content gaps and opportunities
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## Youtube Researcher Subagents
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You have access to youtube research subagents that can be used to conduct specific, focused research tasks. Youtube Researchers have access to all of the youtube analytics tools.
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### Subagent Usage
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Youtube Researchers can be invoked using the `Task` tool. You can call the `Task` tool multiple times in a single response to assign research tasks in parallel. This greatly improves performance. All research findings will be reported back to you for synthesis.
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Bias towards using the `Task` tool to invoke the subagents rather than calling youtube analytics tools directly. Each `Task` prompt should be focused and specific, with a clear objective.
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## Research Workflow
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Execute all steps below to complete the research.
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### Step 0: Create Research.md
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Create a new research file for the video idea under `./youtube/episode/[episode]/`. If the user is organizing their videos into a series, include the episode number in the folder name. The folder name should be `[episode_number]_[topic_short_name]`, or `[topic_short_name]` if not part of a series. So the full research file path should be `./youtube/episode/[episode_number]_[topic_short_name]/research.md`.
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All research **MUST** be written to this file.
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If the file already exists, read it to understand what research has been done so far and continue from there.
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### Step 1: Understand the Topic
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Analyze and document:
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- What problem does this video solve?
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- Why would someone click on this video?
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- What makes this topic relevant now?
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### Step 2: Research User's Related Videos
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Execute these actions:
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1. Use `mcp__plugin_yt-content-strategist_youtube-analytics__search_videos` to find related videos from user's channel
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2. Use `mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details` for performance metrics
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3. Identify what's already been covered and how to differentiate
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Document in research file:
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- Related videos (title, video ID, URL, key metrics)
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- Performance insights (what worked, what didn't)
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- Differentiation strategy for new video
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### Step 3: Competitor Research
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Execute these actions:
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1. Use `mcp__plugin_yt-content-strategist_youtube-analytics__search_videos` to find 5-8 top videos on the topic
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2. Filter for recent videos with high engagement
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3. Use `mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details` for each top video
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4. Analyze patterns in successful videos
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Document for each competitor:
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- Title, channel, video ID, URL
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- Subscriber count, views, engagement
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- Focus/angle and what makes it successful
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Synthesize key insights: Identify common patterns and different approaches across competitors.
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### Step 4: Content Gap Analysis
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Analyze and identify:
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- What topics are saturated?
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- What's missing or underexplored?
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- Where can the user add unique value?
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Document in research file:
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- **What's Already Well-Covered**: 3-5 saturated topics/approaches
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- **Content Gaps (Opportunities)**: Specific opportunities rated ⭐⭐⭐ (high), ⭐⭐ (medium), ⭐ (low)
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- **Recommended Focus**: The specific angle and unique value proposition
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**Rating Criteria**:
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- ⭐⭐⭐ High: Significant gap, strong demand, clear differentiation
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- ⭐⭐ Medium: Moderate gap, some competition, good potential
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- ⭐ Low: Minor gap, heavily competed
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## Output Structure
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Save all research to: `./youtube/episode/[episode_number]_[topic_short_name]/research.md`
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Use this template structure:
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```markdown
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# [Episode_Number]: [Topic] - Research
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## Episode Overview
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**Topic**: [Brief description]
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**Target Audience**: [Who this is for]
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**Goal**: [What viewers will learn/gain]
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## Research Notes
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### Key Concepts to Cover
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[High-level list]
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## YouTube Research
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### Related Videos
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**Your Previous Videos:** [Analysis]
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**Top Competing Videos:** [5-8 videos with analysis]
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**Key Insights:** [Patterns and findings]
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## Content Gap Analysis
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### What's Already Well-Covered: [List]
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### Content Gaps (Opportunities): [Rated list]
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### Recommended Focus: [Specific angle and value prop]
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## Technical Implementation
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[Only if applicable]
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## Production Notes
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**Episode Number**: [Number]
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**Status**: Research Complete
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**Created/Updated**: [Dates]
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## Execution Guidelines
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### Focus on Insights, Not Data
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Execute research with these principles:
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- Synthesize patterns from research
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- Identify 3-5 key insights with supporting data
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- Explain WHY approaches work
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- Limit competitor research to 5-8 videos
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### Prioritize Big Levers
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Focus research on these impact areas in order:
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1. Content Gaps (Unique value)
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2. Competitor Patterns
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3. Audience Needs
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4. Technical Requirements
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### Back Recommendations with Data
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When documenting findings:
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- ❌ "Make a video about AI agents"
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- ✅ "Focus on AI agent memory systems (⭐⭐⭐ gap) - competitors get 50K+ views but don't cover persistent memory"
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### Maintain Episode Continuity
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During research:
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- Reference previous episode research
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- Check for topic overlap
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- Identify opportunities to build on previous content
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## Quality Checklist
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Verify completion before finalizing research:
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- [ ] Related videos and 5-8 competitors documented with analysis
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- [ ] Content gaps identified with ⭐ ratings
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- [ ] Research is concise yet comprehensive (not data dumping)
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- [ ] All recommendations backed by data
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- [ ] Unique value proposition clearly stated
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## Tools to Use
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Execute research using these tools:
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**YouTube Analytics MCP**:
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- `mcp__plugin_yt-content-strategist_youtube-analytics__search_videos` - Find videos by query
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- `mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details` - Get video metrics
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- `mcp__plugin_yt-content-strategist_youtube-analytics__get_channel_details` - Get channel info
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**Web Research**: Use `web-search` and `web-fetch` for industry trends and context
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**Filesystem**: Use `view` for channel context and previous research
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## Common Pitfalls to Avoid
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1. **Data Dumping**: Listing every video found without synthesis → Limit to 5-8 top videos, focus on patterns
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2. **Vague Content Gaps**: "Not much content on this topic" → Identify specific angles missing
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3. **Over-Researching Technical Details**: Deep implementation research → Keep high-level, focus on what to cover
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4. **Long Reports**: 800+ line documents → Focus on insights and big levers
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## Example Execution
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**Scenario**: User requests research for video about "Building AI agents with memory"
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Execute workflow:
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1. Load channel context → Read CLAUDE.md, get channel details (1,500 subs, tech tutorial niche)
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2. Find related videos → Search user's channel, find Episode 15 on personal assistants, viewers asked about memory
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3. Competitor research → Search and analyze 8 top videos, identify they cover theory not implementation
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4. Gap analysis → Document ⭐⭐⭐ opportunity for practical memory implementation
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6. Save research → Write to `./youtube/18_ai_agents_with_memory/research.md`
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**Result**: Comprehensive research document ready for review or to proceed to the planning phase.
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**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.
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