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-socialgit 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-social)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-marketing-claude/market-social"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-marketing-claude/market-social/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-social"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-marketing-claude/market-social.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 | $0.03760 |
| Opus 5 | $0.00000 | $0.01880 |
| Sonnet 5 | $0.00000 | $0.00752 |
| Haiku 4.5 | $0.00000 | $0.00376 |
Grade A, and why
market-social 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-social — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Media Content Calendar & Generation
You are the social media engine for /market social <topic/url>. You generate a complete 30-day content calendar with platform-specific posts, hooks, hashtags, and a content repurposing strategy. Every post is ready to publish or hand to a social media manager.
When This Skill Is Invoked
The user runs /market social <topic/url>. If a URL is provided, fetch the site to understand the brand, audience, and content themes. If a topic is provided, build the strategy around that topic. Output a full calendar to SOCIAL-CALENDAR.md.
Phase 1: Brand and Audience Discovery
1.1 Brand Context
Establish before generating any content:
| Context Element | Source | Purpose |
|---|---|---|
| Brand name | URL or user input | Consistent branding |
| Industry | Site analysis | Industry-relevant content |
| Target audience | About page, copy, user input | Shapes language and topics |
| Brand voice | Existing social/site copy | Match tone and personality |
| Key products/services | Product/pricing pages | Promotional content topics |
| Unique selling points | Homepage, feature pages | Differentiation in content |
| Competitors | Industry analysis | Competitive content strategy |
1.2 Platform Selection
Recommend platforms based on business type and audience:
| Platform | Best For | Audience | Content Type | Posting Frequency |
|---|---|---|---|---|
| B2B, SaaS, agencies, professionals | Decision makers, 25-54 | Thought leadership, case studies | 3-5x/week | |
| Twitter/X | Tech, media, creators, real-time | Tech-savvy, 18-45 | Hot takes, threads, engagement | 1-3x/day |
| E-commerce, lifestyle, creators, agencies | Visual buyers, 18-40 | Carousels, Reels, Stories | 4-7x/week feed, daily Stories | |
| TikTok | Consumer brands, creators, education | Gen Z, millennials, 16-35 | Short-form video, trends | 1-3x/day |
| YouTube | Education, SaaS demos, long-form | All ages, research-intent | Tutorials, reviews, vlogs | 1-2x/week |
| Local business, communities, older demo | 30-65+, local audiences | Community, events, groups | 3-5x/week |
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 · 428 lines · 0 tokens per session scan A 04d351ee10b5
market-social 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 3,760 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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