content-planner

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Chinese

Content Planner

内容策划工具

Orchestrate parallel research across X, Instagram, YouTube, and TikTok, then aggregate findings into content ideas and platform-specific playbooks.

统筹在X、Instagram、YouTube和TikTok平台上的并行研究，然后将研究结果整合为内容创意和各平台专属手册。

Prerequisites

前置条件

Same as individual research skills:

```
APIFY_TOKEN
```
for X, Instagram, and TikTok research
```
TUBELAB_API_KEY
```
for YouTube research
```
GEMINI_API_KEY
```
for video analysis
Accounts configured in
```
.claude/context/
```
for each platform

CRITICAL - Subagent Environment Setup: Each subagent must load environment variables from the

.env

file in the

head-of-marketing

working directory before executing any API calls:

bash

export $(cat .env | grep -v '^#' | xargs)

与各独立研究技能要求相同：

用于X、Instagram和TikTok研究的
```
APIFY_TOKEN
```
用于YouTube研究的
```
TUBELAB_API_KEY
```
用于视频分析的
```
GEMINI_API_KEY
```
在
```
.claude/context/
```
中为每个平台配置好账号

关键 - 子Agent环境配置：每个子Agent在执行任何API调用前，必须从

head-of-marketing

工作目录下的

.env

文件加载环境变量：

bash

export $(cat .env | grep -v '^#' | xargs)

Workflow

工作流程

1. Read User Context

1. 读取用户上下文

Read all files in

.claude/context/

to understand the user's niche, target audience, and accounts to research. Pass this context to each subagent.

读取

.claude/context/

下的所有文件，了解用户的细分领域、目标受众以及需要研究的账号。将此上下文传递给每个子Agent。

2. Create Master Run Folder

2. 创建主运行文件夹

bash

RUN_FOLDER="content-plans/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"

bash

RUN_FOLDER="content-plans/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"

3. Launch Research Subagents in Parallel

3. 并行启动研究子Agent

Use the Task tool to launch 4 subagents simultaneously:

Subagent 1 - X Research:

Execute the x-research skill:
1. Create run folder in x-research/
2. Fetch tweets (30 days, 100 max per account)
3. Analyze for outliers
4. Run video analysis if video content found
5. Generate report

Return: The run folder path and a JSON summary with:
- run_folder: path to the run folder
- total_posts: number analyzed
- outlier_count: outliers found
- top_topics: top 5 hashtags/keywords

Subagent 2 - Instagram Research:

Execute the instagram-research skill:
1. Create run folder in instagram-research/
2. Fetch reels (30 days, 50 per account)
3. Analyze for outliers
4. Run video analysis on top 5
5. Generate report

Return: The run folder path and a JSON summary with:
- run_folder: path to the run folder
- total_posts: number analyzed
- outlier_count: outliers found
- top_topics: top 5 hashtags/keywords

Subagent 3 - YouTube Research:

Execute the youtube-research skill:
1. Read channel context from .claude/context/youtube-channel.md
2. Analyze channel for keywords
3. Search for outliers
4. Filter to top 3 relevant videos
5. Run video analysis
6. Generate report

Return: The run folder path and a JSON summary with:
- run_folder: path to the run folder
- total_videos: number analyzed
- outlier_count: outliers found
- top_topics: top 5 keywords

Subagent 4 - TikTok Research:

Execute the tiktok-research skill:
1. Create run folder in tiktok-research/
2. Fetch videos (30 days, 50 per account)
3. Analyze for outliers
4. Run video analysis on top 5
5. Generate report

Return: The run folder path and a JSON summary with:
- run_folder: path to the run folder
- total_videos: number analyzed
- outlier_count: outliers found
- top_topics: top 5 hashtags/sounds/keywords

使用任务工具同时启动4个子Agent：

子Agent 1 - X平台研究：

执行x-research技能：
1. 在x-research/下创建运行文件夹
2. 获取推文（近30天，每个账号最多100条）
3. 分析异常内容
4. 若发现视频内容则运行视频分析
5. 生成报告

返回结果：运行文件夹路径和包含以下内容的JSON摘要：
- run_folder: 运行文件夹路径
- total_posts: 分析的帖子数量
- outlier_count: 发现的异常内容数量
- top_topics: 排名前5的话题标签/关键词

子Agent 2 - Instagram平台研究：

执行instagram-research技能：
1. 在instagram-research/下创建运行文件夹
2. 获取Reels视频（近30天，每个账号50条）
3. 分析异常内容
4. 对排名前5的内容运行视频分析
5. 生成报告

返回结果：运行文件夹路径和包含以下内容的JSON摘要：
- run_folder: 运行文件夹路径
- total_posts: 分析的帖子数量
- outlier_count: 发现的异常内容数量
- top_topics: 排名前5的话题标签/关键词

子Agent 3 - YouTube平台研究：

执行youtube-research技能：
1. 从.claude/context/youtube-channel.md读取频道上下文
2. 分析频道关键词
3. 搜索异常内容
4. 筛选出排名前3的相关视频
5. 运行视频分析
6. 生成报告

返回结果：运行文件夹路径和包含以下内容的JSON摘要：
- run_folder: 运行文件夹路径
- total_videos: 分析的视频数量
- outlier_count: 发现的异常内容数量
- top_topics: 排名前5的关键词

子Agent 4 - TikTok平台研究：

执行tiktok-research技能：
1. 在tiktok-research/下创建运行文件夹
2. 获取视频（近30天，每个账号50条）
3. 分析异常内容
4. 对排名前5的内容运行视频分析
5. 生成报告

返回结果：运行文件夹路径和包含以下内容的JSON摘要：
- run_folder: 运行文件夹路径
- total_videos: 分析的视频数量
- outlier_count: 发现的异常内容数量
- top_topics: 排名前5的话题标签/音效/关键词

4. Collect Research Results

4. 收集研究结果

After all subagents complete, read from each platform's latest run folder:

x-research/{latest}/
├── outliers.json
└── video-analysis.json (if exists)

instagram-research/{latest}/
├── outliers.json
└── video-analysis.json

youtube-research/{latest}/
├── outliers.json
└── video-analysis.json

tiktok-research/{latest}/
├── outliers.json
└── video-analysis.json

所有子Agent完成后，从每个平台的最新运行文件夹读取内容：

x-research/{latest}/
├── outliers.json
└── video-analysis.json (若存在)

instagram-research/{latest}/
├── outliers.json
└── video-analysis.json

youtube-research/{latest}/
├── outliers.json
└── video-analysis.json

tiktok-research/{latest}/
├── outliers.json
└── video-analysis.json

5. Generate Content Ideas

5. 生成内容创意

Read

references/content-ideas-template.md

for the full template structure.

Key aggregation tasks:

Extract topics from each platform's outliers
Cross-reference to find topics appearing on multiple platforms
Identify X-sourced emerging ideas (high X engagement, low presence elsewhere)

Calculate opportunity scores for X ideas:

opportunity_score = (x_engagement × 1.5) / (instagram_saturation + youtube_saturation + tiktok_saturation + 1)

```
instagram_saturation
```
: 0 (not present), 0.5 (low), 1 (medium), 1.5 (high)
```
youtube_saturation
```
: same scale
```
tiktok_saturation
```
: same scale

Generate 2-week calendar with platform-specific content suggestions

Write to:

{RUN_FOLDER}/content-ideas.md

读取

references/content-ideas-template.md

获取完整模板结构。

关键整合任务：

提取话题：从每个平台的异常内容中提取话题
交叉引用：找出在多个平台出现的话题
识别源自X平台的新兴创意（在X平台互动量高，在其他平台曝光度低）

为X平台创意计算机会得分：

opportunity_score = (x_engagement × 1.5) / (instagram_saturation + youtube_saturation + tiktok_saturation + 1)

```
instagram_saturation
```
: 0（未出现）、0.5（低）、1（中）、1.5（高）
```
youtube_saturation
```
: 相同评分标准
```
tiktok_saturation
```
: 相同评分标准

生成2周内容日历：包含针对各平台的内容建议

写入路径：

{RUN_FOLDER}/content-ideas.md

6. Generate Platform Playbooks

6. 生成平台专属手册

For each platform, read

references/playbook-template.md

and generate:

```
{RUN_FOLDER}/x-playbook.md
```
```
{RUN_FOLDER}/instagram-playbook.md
```
```
{RUN_FOLDER}/youtube-playbook.md
```
```
{RUN_FOLDER}/tiktok-playbook.md
```

Each playbook extracts from the platform's research:

Winning hooks with replicable formulas (from video-analysis.json)
Format analysis and content patterns
Content structure breakdowns
CTA strategies
Trending topics and hashtags
Top 15 outliers with analysis
Actionable takeaways

针对每个平台，读取

references/playbook-template.md

并生成：

```
{RUN_FOLDER}/x-playbook.md
```
```
{RUN_FOLDER}/instagram-playbook.md
```
```
{RUN_FOLDER}/youtube-playbook.md
```
```
{RUN_FOLDER}/tiktok-playbook.md
```

每个手册从对应平台的研究结果中提取以下内容：

可复制的高吸引力钩子公式（来自video-analysis.json）
格式分析与内容模式
内容结构拆解
CTA策略
热门话题与话题标签
排名前15的异常内容及分析
可执行的行动要点

7. Present Summary

7. 呈现总结

Output to user:

Total content analyzed across all platforms
Number of outliers identified per platform
Key cross-platform insights (2-3 bullets)
Top 3 emerging ideas from X
Links to all generated files

向用户输出：

全平台分析的内容总量
各平台识别出的异常内容数量
跨平台关键洞察（2-3条要点）
源自X平台的Top3新兴创意
所有生成文件的链接

Output Structure

输出结构

content-plans/
└── {YYYY-MM-DD_HHMMSS}/
    ├── content-ideas.md          # Cross-platform ideas (X-primary)
    ├── x-playbook.md             # X/Twitter intelligence playbook
    ├── instagram-playbook.md     # Instagram intelligence playbook
    ├── youtube-playbook.md       # YouTube intelligence playbook
    └── tiktok-playbook.md        # TikTok intelligence playbook

content-plans/
└── {YYYY-MM-DD_HHMMSS}/
    ├── content-ideas.md          # 跨平台创意（以X平台为核心）
    ├── x-playbook.md             # X/Twitter情报手册
    ├── instagram-playbook.md     # Instagram情报手册
    ├── youtube-playbook.md       # YouTube情报手册
    └── tiktok-playbook.md        # TikTok情报手册

Cross-Platform Topic Matching

跨平台话题匹配

To identify cross-platform winners:

Extract keywords/hashtags from each platform's outliers
Normalize terms (lowercase, remove # and @)
Find intersection of high-frequency terms
Score by combined engagement across platforms

识别跨平台热门内容的方法：

从各平台的异常内容中提取关键词/话题标签
标准化术语（转为小写，移除#和@）
找出高频出现的交叉话题
根据跨平台的综合互动量计算得分

Quick Reference

快速参考

Full orchestration:

Create master run folder
Launch 4 research subagents in parallel (Task tool with 4 invocations)
Wait for all subagents to complete
Read all outliers.json and video-analysis.json files
Generate content-ideas.md using cross-platform analysis
Generate 4 platform playbooks
Present summary to user

完整统筹流程：

创建主运行文件夹
并行启动4个研究子Agent（使用任务工具发起4次调用）
等待所有子Agent完成
读取所有outliers.json和video-analysis.json文件
通过跨平台分析生成content-ideas.md
生成4份平台专属手册
向用户呈现总结