youtube-research-video-topic by manojbajaj95/claude-gtm-plugin
npx skills add https://github.com/manojbajaj95/claude-gtm-plugin --skill youtube-research-video-topic此技能专为 YouTube 视频主题进行纯粹的研究。执行所有步骤以生成可操作的见解,识别内容缺口并分析竞争对手。此技能仅专注于研究——它不生成标题、缩略图或钩子。
核心原则:专注于洞察和关键杠杆,而非数据堆砌。研究应全面而简洁,有数据支撑,旨在为战略决策提供信息。
在以下情况使用此技能:
您可以访问 YouTube 研究子代理,用于执行特定、聚焦的研究任务。YouTube 研究员可以访问所有 YouTube 分析工具。
可以使用 Task 工具调用 YouTube 研究员。您可以在单个响应中多次调用 Task 工具来并行分配研究任务。这能极大地提高性能。所有研究发现都将汇总报告给您。
倾向于使用 Task 工具来调用子代理,而不是直接调用 YouTube 分析工具。每个 Task 提示都应聚焦且具体,目标明确。
执行以下所有步骤以完成研究。
在 ./youtube/episode/[episode]/ 下为视频想法创建一个新的研究文件。如果用户将视频组织成系列,请在文件夹名称中包含集数。文件夹名称应为 ,如果不是系列的一部分,则为 。因此,完整的研究文件路径应为 。
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[episode_number]_[topic_short_name][topic_short_name]./youtube/episode/[episode_number]_[topic_short_name]/research.md所有研究必须写入此文件。
如果文件已存在,请读取它以了解已完成的研究内容,并从中继续。
分析并记录:
执行以下操作:
mcp__plugin_yt-content-strategist_youtube-analytics__search_videos 查找用户频道中的相关视频mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details 获取性能指标在研究文件中记录:
执行以下操作:
mcp__plugin_yt-content-strategist_youtube-analytics__search_videos 查找该主题下的 5-8 个顶级视频mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details为每个竞争对手记录:
综合关键洞察:识别竞争对手之间的共同模式和不同方法。
分析并识别:
在研究文件中记录:
评级标准:
将所有研究保存至:./youtube/episode/[episode_number]_[topic_short_name]/research.md
使用此模板结构:
# [Episode_Number]: [Topic] - 研究
## 剧集概述
**主题**:[简要描述]
**目标受众**:[面向谁]
**目标**:[观众将学到/获得什么]
## 研究笔记
### 需涵盖的关键概念
[高层次列表]
## YouTube 研究
### 相关视频
**您之前的视频**:[分析]
**顶级竞争视频**:[5-8 个视频及分析]
**关键洞察**:[模式和发现]
## 内容缺口分析
### 已充分涵盖的内容:[列表]
### 内容缺口(机会):[评级列表]
### 推荐焦点:[具体角度和价值主张]
## 技术实现
[仅当适用时]
## 制作笔记
**剧集编号**:[编号]
**状态**:研究完成
**创建/更新**:[日期]
## 执行指南
### 专注于洞察,而非数据
遵循以下原则执行研究:
- 综合研究中的模式
- 识别 3-5 个有数据支持的关键洞察
- 解释方法为何有效
- 将竞争对手研究限制在 5-8 个视频
### 优先考虑关键杠杆
按顺序将研究重点放在以下影响领域:
1. 内容缺口(独特价值)
2. 竞争对手模式
3. 受众需求
4. 技术要求
### 用数据支撑建议
记录发现时:
- ❌ "制作一个关于 AI 代理的视频"
- ✅ "专注于 AI 代理记忆系统(⭐⭐⭐ 缺口)- 竞争对手获得 5 万+ 观看量但未涵盖持久性记忆"
### 保持剧集连续性
在研究过程中:
- 参考之前的剧集研究
- 检查主题重叠
- 识别基于先前内容进行构建的机会
## 质量检查清单
在最终确定研究前验证完成情况:
- [ ] 相关视频和 5-8 个竞争对手已记录并附有分析
- [ ] 内容缺口已识别并附有 ⭐ 评级
- [ ] 研究简洁而全面(非数据堆砌)
- [ ] 所有建议均有数据支撑
- [ ] 独特价值主张表述清晰
## 使用的工具
使用以下工具执行研究:
**YouTube 分析 MCP**:
- `mcp__plugin_yt-content-strategist_youtube-analytics__search_videos` - 按查询查找视频
- `mcp__plugin_yt-content-strategist_youtube-analytics__get_video_details` - 获取视频指标
- `mcp__plugin_yt-content-strategist_youtube-analytics__get_channel_details` - 获取频道信息
**网络研究**:使用 `web-search` 和 `web-fetch` 获取行业趋势和背景信息
**文件系统**:使用 `view` 获取频道背景和先前研究
## 需避免的常见陷阱
1. **数据堆砌**:列出找到的每个视频而不进行综合 → 限制在 5-8 个顶级视频,专注于模式
2. **模糊的内容缺口**:"关于这个主题的内容不多" → 识别缺失的具体角度
3. **过度研究技术细节**:深入研究实现细节 → 保持高层次,专注于要涵盖的内容
4. **冗长的报告**:800+ 行的文档 → 专注于洞察和关键杠杆
## 执行示例
**场景**:用户请求研究关于"构建具有记忆的 AI 代理"的视频
执行工作流:
1. 加载频道背景 → 读取 CLAUDE.md,获取频道详情(1500 订阅者,技术教程领域)
2. 查找相关视频 → 搜索用户频道,找到关于个人助手的第 15 集,观众询问了关于记忆的问题
3. 竞争对手研究 → 搜索并分析 8 个顶级视频,识别出它们涵盖理论而非实现
4. 缺口分析 → 记录关于实践性记忆实现的 ⭐⭐⭐ 机会
6. 保存研究 → 写入 `./youtube/18_ai_agents_with_memory/research.md`
**结果**:全面的研究文档已准备就绪,可供审查或进入规划阶段。
**下一步**:如果用户要求规划视频,请调用 `youtube-plan-new-video` 技能,基于此研究生成标题、缩略图和钩子概念。
每周安装量
117
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2026年3月11日
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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.
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.
Use this skill when:
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.
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.
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.
Execute all steps below to complete the research.
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.
All research MUST be written to this file.
If the file already exists, read it to understand what research has been done so far and continue from there.
Analyze and document:
Execute these actions:
mcp__plugin_yt-content-strategist_youtube-analytics__search_videos to find related videos from user's channelmcp__plugin_yt-content-strategist_youtube-analytics__get_video_details for performance metricsDocument in research file:
Execute these actions:
mcp__plugin_yt-content-strategist_youtube-analytics__search_videos to find 5-8 top videos on the topicmcp__plugin_yt-content-strategist_youtube-analytics__get_video_details for each top videoDocument for each competitor:
Synthesize key insights: Identify common patterns and different approaches across competitors.
Analyze and identify:
Document in research file:
Rating Criteria :
Save all research to: ./youtube/episode/[episode_number]_[topic_short_name]/research.md
Use this template structure:
# [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.
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Mar 11, 2026
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