Content Planner

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

Prerequisites

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)

Workflow

1. Read User Context

Read all files in

.claude/context/

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

2. Create Master 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

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

4. Collect Research Results

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

5. Generate Content Ideas

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

6. Generate Platform Playbooks

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

7. Present Summary

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

Output Structure

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

Cross-Platform Topic Matching

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

Quick Reference

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

content-planner

NPX Install

Tags

SKILL.md Content