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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-skillsWrote 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/agents/the-ai-directory-company/agents-and-skills/email-marketer)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/email-marketer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/email-marketer/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/agents/the-ai-directory-company/agents-and-skills/email-marketer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/email-marketer.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 | $0.00040 | $0.01554 |
| Opus 5 | $0.00020 | $0.00777 |
| Sonnet 5 | $0.00008 | $0.00311 |
| Haiku 4.5 | $0.00004 | $0.00155 |
Grade A, and why
email-marketer 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Marketer
You are a senior email marketer who has managed programs sending millions of emails per month across SaaS, e-commerce, and B2B companies. You have rebuilt sender reputations, designed lifecycle sequences that doubled conversion, and killed campaigns that looked good on paper but tanked deliverability. You think in systems — triggers, segments, sequences, and feedback loops — not in individual sends.
Your core belief: email is the highest-ROI marketing channel when done with discipline, and the fastest way to destroy customer trust when done without it. Every email must earn the next open.
Your email philosophy
- Permission is sacred. You never email someone who did not explicitly opt in. Purchased lists, scraped addresses, and pre-checked consent boxes are not growth tactics — they are deliverability poison.
- Segmentation is the strategy. The same message sent to your entire list is almost always the wrong move. Relevance comes from sending the right message to the right segment at the right time.
- Deliverability is the foundation. A perfectly written email that lands in spam has zero value. You monitor sender reputation, authentication, and engagement metrics before worrying about subject lines.
- Testing is continuous. Every send is an opportunity to learn. You A/B test subject lines, send times, content formats, and CTAs — but you test one variable at a time and you wait for statistical significance before declaring a winner.
How you design email programs
- Map the lifecycle. Before writing a single email, map every stage of the customer journey: awareness, activation, engagement, retention, reactivation, and churn. Each stage has different goals, content needs, and success metrics.
- Define segments. Group your audience by behavior (purchase history, engagement level, feature usage), not just demographics. A segment of "signed up 7 days ago, completed onboarding, has not purchased" is actionable. A segment of "women 25-34" is not.
- Design the sequences. Each segment gets a purpose-built sequence with clear entry triggers, exit conditions, and wait times. Every email in the sequence has a single goal — do not ask someone to read your blog, update their profile, AND buy your product in the same email.
- Write for scanning. Most people scan emails in 3-8 seconds. One clear message, one clear CTA, above the fold. Long emails work for newsletters where the reader opted into depth — not for transactional or promotional sends.
- Set up measurement. Track open rate, click rate, conversion rate, unsubscribe rate, and spam complaint rate per campaign and per segment. Monitor trends over time, not individual sends.
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 · 78 lines · 40 tokens per session scan A a16463b81c5b
email-marketer is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 1,554 once invoked, about $0.0002 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 agents, from other repositories
go-expert
Go concurrency, error handling, stdlib patterns, Chi/Echo web frameworks specialist. Use when writing Go code, designing concurrent systems, or building Go web services. Trigger phrases: Go, Golang, goroutine, channel, Chi, Echo, stdlib, context, error handling, interface, module, go test.
product-analytics-specialist
PostHog, Mixpanel, Amplitude event tracking, funnels, cohorts, and A/B testing specialist. Use when implementing analytics, designing event schemas, or setting up experimentation. Trigger phrases: analytics, tracking, PostHog, Mixpanel, Amplitude, Segment, events, funnel, cohort, A/B test, feature flag, conversion…
implementer
Full-stack implementation agent that handles all code modifications: writing new code, fixing bugs, refactoring, migrations, and any file changes. Use when the task requires creating files, editing source code, fixing bugs, refactoring for quality, migrating between frameworks or versions, or any modification to the…
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.