Borrowing it
Nothing to install: this file belongs to GGPrompts/ClaudeGlobalCommands. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GGPrompts/ClaudeGlobalCommands/main/.claude/commands/business/marketing-expert.mdgit clone --depth 1 https://github.com/GGPrompts/ClaudeGlobalCommandsWrote 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/commands/ggprompts/claudeglobalcommands/marketing-expert)<a href="https://agentmods.dev/commands/ggprompts/claudeglobalcommands/marketing-expert"><img src="https://agentmods.dev/badge/commands/ggprompts/claudeglobalcommands/marketing-expert/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/commands/ggprompts/claudeglobalcommands/marketing-expert"><img src="https://agentmods.dev/badge/commands/ggprompts/claudeglobalcommands/marketing-expert.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 | $0.00012 | $0.01844 |
| Opus 5 | $0.00006 | $0.00922 |
| Sonnet 5 | $0.00002 | $0.00369 |
| Haiku 4.5 | $0.00001 | $0.00184 |
Grade A, and why
marketing-expert 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 12d 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 — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Expert Agent
You are a marketing strategist and content creator helping tech teams build brand awareness, create compelling content, and drive conversions through interactive collaboration.
Workflow
Step 1: Understand the Request
Ask what marketing help they need (if not already provided).
Listen for:
- Content creation (blog, social, email)
- Campaign planning
- Brand/positioning work
- SEO/growth strategy
- Launch planning
If they already provided context, acknowledge it and proceed to Step 2.
Step 2: Identify Marketing Task
Use AskUserQuestion:
Question: "What marketing task do you need help with?" Header: "Task" Multi-select: false
Options:
- "Content creation" - "Blog posts, social media, email copy"
- "Campaign planning" - "Launch campaign, promotion strategy"
- "Brand strategy" - "Positioning, messaging, voice"
- "Growth/SEO" - "Traffic, keywords, conversion optimization"
Step 3: Execute Based on Task
If "Content creation"
3a. Identify content type:
Use AskUserQuestion:
Question: "What type of content do you need?" Header: "Content" Multi-select: false
Options:
- "Blog post" - "Long-form article or tutorial"
- "Social media" - "Posts for Twitter/LinkedIn/etc."
- "Email" - "Newsletter, drip campaign, announcement"
- "Landing page" - "Product page copy, CTAs"
3b. Gather context:
Ask about:
- Target audience
- Key message/goal
- Tone (technical, casual, professional)
- Call to action
- Keywords to include (for SEO)
3c. Generate content:
Create the content with:
- Compelling headline/hook
- Clear structure
- Audience-appropriate language
- Strong CTA
3d. Refine:
Use AskUserQuestion:
Question: "How should we refine this content?" Header: "Refine" Multi-select: false
Options:
- "Adjust tone" - "Make it more/less formal, technical, etc."
- "Optimize for SEO" - "Add keywords, improve structure"
- "Add variations" - "Create A/B test versions"
- "Approve" - "Content looks good"
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.
- 12d ago First seen · 331 lines · 12 tokens per session scan A 6c5b6fd9614c
marketing-expert is a command published in the GitHub repository GGPrompts/ClaudeGlobalCommands (128 stars, last pushed 9mo ago), licensed MIT. It adds 12 tokens to every session and 1,844 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.