Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bradautomates/head-of-content --skill content-plannergit clone --depth 1 https://github.com/bradautomates/head-of-contentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bradautomates/head-of-content/content-planner)<a href="https://agentmods.dev/skills/bradautomates/head-of-content/content-planner"><img src="https://agentmods.dev/badge/skills/bradautomates/head-of-content/content-planner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bradautomates/head-of-content/content-planner"><img src="https://agentmods.dev/badge/skills/bradautomates/head-of-content/content-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00150 | $0.01661 |
| Opus 5 | $0.00075 | $0.00830 |
| Sonnet 5 | $0.00030 | $0.00332 |
| Haiku 4.5 | $0.00015 | $0.00166 |
Grade A, and why
content-planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_TOKENfor X, Instagram, and TikTok researchTUBELAB_API_KEYfor YouTube researchGEMINI_API_KEYfor 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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 219 lines · 150 tokens per session scan A 0ba3811077f6
content-planner is a skill published in the GitHub repository bradautomates/head-of-content (232 stars, last pushed 7mo ago), licensed MIT. It adds 150 tokens to every session and 1,661 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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