Content Planner

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:

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

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:

1. Extract topics from each platform's outliers
2. Cross-reference to find topics appearing on multiple platforms
3. Identify X-sourced emerging ideas (high X engagement, low presence elsewhere)
4. 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

1. 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:

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

Quick Reference

Full orchestration:

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bradautomates
• Source: bradautomates/head-of-content
• License: MIT

Install and usage instructions live in the source repository linked above.