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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zubair-trabzada/ai-marketing-claudenpx agentmods add skills/zubair-trabzada/ai-marketing-claude/market-seoWrote 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-seo)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-marketing-claude/market-seo"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-marketing-claude/market-seo/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-seo"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-marketing-claude/market-seo.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 | $0.04276 |
| Opus 5 | $0.00000 | $0.02138 |
| Sonnet 5 | $0.00000 | $0.00855 |
| Haiku 4.5 | $0.00000 | $0.00428 |
Grade A, and why
market-seo 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-seo — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Content Audit
Skill Purpose
Perform a comprehensive SEO audit of a webpage or website, covering on-page SEO, content quality (E-E-A-T), keyword analysis, technical SEO, and content strategy. This skill combines automated analysis via scripts/analyze_page.py with expert-level manual review to produce an actionable SEO audit document.
When to Use
- User provides a URL and asks for SEO analysis, audit, or recommendations
- User wants to improve organic search rankings and traffic
- User asks about on-page SEO, meta tags, content quality, or technical SEO
- User wants a content gap analysis or content strategy recommendations
- Triggered by
/market seo <url>or/market seo
How to Execute
Step 1: Run Automated Analysis
Use the Python analysis script to gather baseline data:
python3 scripts/analyze_page.py <url>
This script extracts:
- Title tag and meta description
- Open Graph tags
- Heading hierarchy (H1-H6)
- Links (internal and external)
- Images and alt text status
- Forms and CTAs
- Schema/structured data
- Social links
- Tracking scripts
- Viewport meta tag (mobile-friendliness indicator)
- Canonical tag
- Robots meta directives
Capture the JSON output and use it as the foundation for the manual analysis.
Step 2: On-Page SEO Checklist
Evaluate each element and score it as Pass, Needs Work, or Fail.
Title Tag
| Criteria | Best Practice | Check |
|---|---|---|
| Exists | Every page must have a unique title tag | Pass/Fail |
| Length | 50-60 characters (displays fully in SERPs) | Pass/Needs Work/Fail |
| Primary keyword | Contains the primary target keyword | Pass/Needs Work/Fail |
| Keyword position | Primary keyword appears near the beginning | Pass/Needs Work/Fail |
| Brand name | Includes brand name (typically at the end, separated by pipe or dash) | Pass/Needs Work/Fail |
| Uniqueness | Different from other pages on the site | Pass/Fail |
| Compelling | Would a searcher want to click this? | Pass/Needs Work/Fail |
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 · 483 lines · 0 tokens per session scan A b269401df8da
market-seo 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 4,276 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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