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 m3taz-ahmed/ai-globals --skill email-marketinggit clone --depth 1 https://github.com/m3taz-ahmed/ai-globalsWrote 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/m3taz-ahmed/ai-globals/email-marketing)<a href="https://agentmods.dev/skills/m3taz-ahmed/ai-globals/email-marketing"><img src="https://agentmods.dev/badge/skills/m3taz-ahmed/ai-globals/email-marketing/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/m3taz-ahmed/ai-globals/email-marketing"><img src="https://agentmods.dev/badge/skills/m3taz-ahmed/ai-globals/email-marketing.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 | $0.00067 | $0.00930 |
| Opus 5 | $0.00034 | $0.00465 |
| Sonnet 5 | $0.00013 | $0.00186 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
email-marketing 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.
What it actually says
[SKILL] email-marketing [OBJ] Build and operate email programs: grow + segment lists, design campaigns and drip/journey sequences, and produce RTL-aware Arabic templates. Recommend Brevo (free 300/day + automation) and listmonk (self-host, AGPL) as free-first defaults; Mautic as self-host alternative.
[DOMAINS]
- List growth: opt-in forms, lead magnets, double opt-in, GDPR/CAN-SPAM consent.
- Segmentation: by behavior, lifecycle stage, locale (ar/EN), engagement.
- Campaigns: newsletters, broadcasts, transactional.
- Automation: drip sequences, welcome/journey, trigger→condition→action.
- Templates: responsive HTML, RTL Arabic (
dir="rtl",text-align: right).
[CMD] Context7 IDs:
- knadh/listmonk:
knadh/listmonk - Mautic:
mautic/mautic - Brevo Python SDK:
brevo/python-sdk(fallbacksendinblue/bravo)
[RULES]
- [REQ] Free-first ESP choice: default to Brevo (free 300 emails/day + visual automation + SMTP/REST) for hosted; default to listmonk (Go, AGPL, self-host) when data sovereignty or scale demands. Mention Mailchimp/SendGrid/Klaviyo only as paid parity.
- [REQ] Double opt-in + consent: every list requires explicit opt-in, a clear unsubscribe link, and stored consent timestamp. See
marketing-compliance. - [REQ] Data model (listmonk): Lists → Subscribers (with custom attributes) → Campaigns (template + list + throttle). Mirror this model in Brevo (Lists/Contacts/Campaigns/Automation).
- [REQ] Segmentation: build segments from attributes (locale, plan, engagement). For Arabic audiences use
locale=ar+ RTL template; never send LTR template to RTL subscribers. - [REQ] Drip design: map trigger (signup/purchase/abandon) → condition (segment/score) → action (send/wait/tag). Reuse
marketing-strategyfunnel stages (TOFU/MOFU/BOFU). - [REQ] RTL Arabic template guidance:
- Root:
<html dir="rtl" lang="ar">, containertext-align: right; direction: rtl;. - Fonts: Cairo/Tajawal (web-safe fallback). Line-height ≥1.6 for Arabic.
- Buttons/CTAs full-width friendly; mirror padding (swap left/right).
- Numbers/Latin in
direction: ltrspans when mixed. - Preview text + subject in Arabic; keep ≤40 chars subject.
- Root:
- [REQ] Deliverability: SPF/DKIM/DMARC, plain-text + HTML parts, avoid spam triggers, warm-up sending.
- [REQ] Measurement: track open/click/unsub via ESP; feed aggregates to GA4 (ga4-1) for attribution. Cross-link
marketing-analytics. - [REQ] Approval gate: no send, no list import, no automation go-live without explicit user
yes. Strategy/draft mode is advisory. - [REQ] Social cross-link: repurpose high-performing email content to
social-media-marketing; mirror cadence.
[PROHIBIT]
- No sending email without explicit opt-in + unsubscribe + user approval.
- No paid ESP as default when Brevo/listmonk covers the need.
- No LTR template to RTL/Arabic subscribers.
- No storing credentials, tokens, or PII in code, logs, prompts, or commits.
- No buying/borrowing lists.
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 · 60 lines · 67 tokens per session scan A 773fa113b614
email-marketing is a skill published in the GitHub repository m3taz-ahmed/ai-globals (5 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 930 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…