AI Marketing Suite for Claude Code is a collection of marketing skills and parallel agents that analyze websites and produce copy, email sequences, campaigns, content calendars, competitor research, and reports. Entrepreneurs, agencies, and solo operators use it to run marketing workflows from Claude Code, and the catalogue lists the suite's skills and agents.
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 zubair-trabzada/ai-marketing-claude --skill market-brandgit clone --depth 1 https://github.com/zubair-trabzada/ai-marketing-claudeWrote 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/zubair-trabzada/ai-marketing-claude/market-brand)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-marketing-claude/market-brand"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-marketing-claude/market-brand/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/zubair-trabzada/ai-marketing-claude/market-brand"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-marketing-claude/market-brand.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.03884 |
| Opus 5 | $0.00000 | $0.01942 |
| Sonnet 5 | $0.00000 | $0.00777 |
| Haiku 4.5 | $0.00000 | $0.00388 |
Grade A, and why
market-brand 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 13d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- market-brand — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Voice Analysis and Guidelines Generation
Skill Purpose
Analyze a brand's voice, tone, and messaging across all available channels and generate a comprehensive brand voice guidelines document. This skill examines how a brand communicates, identifies patterns and inconsistencies, and produces actionable guidelines that any writer or marketer can follow to maintain brand consistency.
When to Use
- User wants to understand or document a brand's voice
- User needs brand voice guidelines for a team, freelancers, or agency
- User wants to ensure consistency across marketing channels
- User is rebranding or refining their brand identity
- User wants to compare their brand voice to competitors
- Triggered by
/market brand <url>or/market brand
How to Execute
Step 1: Gather Source Material
To analyze a brand's voice, examine content from multiple sources. Prioritize in this order:
Primary Sources (must analyze):
- Homepage -- The most curated representation of the brand
- About page -- How the brand describes itself
- Product/service pages -- How they present their offerings
Secondary Sources (analyze if available): 4. Blog posts (at least 3-5 recent posts) 5. Social media profiles (bio, recent posts, engagement style) 6. Email newsletters (welcome email, recent sends) 7. Customer-facing copy (error messages, onboarding flows, help docs)
Tertiary Sources: 8. Job postings -- Reveals internal culture and values 9. Press releases -- Formal communication style 10. Ad copy -- Paid messaging approach 11. Video scripts or podcast transcripts -- Spoken brand voice
Use browser tools or the analyze_page.py script to access web content. For social media, check the website for social links and analyze the linked profiles.
Step 2: Voice Dimension Analysis
Map the brand's voice along four primary dimensions. Each dimension is a spectrum, not a binary.
Dimension 1: Formal <-----> Casual
Where does the brand fall on the formality spectrum?
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.
- 13d ago First seen · 472 lines · 0 tokens per session scan A d4dc4754d593
market-brand is a skill published in the GitHub repository zubair-trabzada/ai-marketing-claude (2,639 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,884 tokens. 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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content-pillar-atomizer
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market-ads
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