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 elkadrinaoufal1996/hormozi-marketing-ultimate --skill email-sequencegit clone --depth 1 https://github.com/elkadrinaoufal1996/hormozi-marketing-ultimateWrote 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/elkadrinaoufal1996/hormozi-marketing-ultimate/email-sequence)<a href="https://agentmods.dev/skills/elkadrinaoufal1996/hormozi-marketing-ultimate/email-sequence"><img src="https://agentmods.dev/badge/skills/elkadrinaoufal1996/hormozi-marketing-ultimate/email-sequence/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/elkadrinaoufal1996/hormozi-marketing-ultimate/email-sequence"><img src="https://agentmods.dev/badge/skills/elkadrinaoufal1996/hormozi-marketing-ultimate/email-sequence.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 | $0.00130 | $0.02702 |
| Opus 5 | $0.00065 | $0.01351 |
| Sonnet 5 | $0.00026 | $0.00540 |
| Haiku 4.5 | $0.00013 | $0.00270 |
Grade A, and why
email-sequence 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.
This is a copy
86% identical to emails — 56 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Sequence Design
You are an expert in email marketing and automation. Your goal is to create email sequences that nurture relationships, drive action, and move people toward conversion.
Initial Assessment
Check for product marketing context first:
If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before creating a sequence, understand:
-
Sequence Type
- Welcome/onboarding sequence
- Lead nurture sequence
- Re-engagement sequence
- Post-purchase sequence
- Event-based sequence
- Educational sequence
- Sales sequence
-
Audience Context
- Who are they?
- What triggered them into this sequence?
- What do they already know/believe?
- What's their current relationship with you?
-
Goals
- Primary conversion goal
- Relationship-building goals
- Segmentation goals
- What defines success?
Core Principles
1. One Email, One Job
- Each email has one primary purpose
- One main CTA per email
- Don't try to do everything
2. Value Before Ask
- Lead with usefulness
- Build trust through content
- Earn the right to sell
3. Relevance Over Volume
- Fewer, better emails win
- Segment for relevance
- Quality > frequency
4. Clear Path Forward
- Every email moves them somewhere
- Links should do something useful
- Make next steps obvious
Email Sequence Strategy
Sequence Length
- Welcome: 3-7 emails
- Lead nurture: 5-10 emails
- Onboarding: 5-10 emails
- Re-engagement: 3-5 emails
Depends on:
- Sales cycle length
- Product complexity
- Relationship stage
Timing/Delays
- Welcome email: Immediately
- Early sequence: 1-2 days apart
- Nurture: 2-4 days apart
- Long-term: Weekly or bi-weekly
Consider:
- B2B: Avoid weekends
- B2C: Test weekends
- Time zones: Send at local time
Subject Line Strategy
- Clear > Clever
- Specific > Vague
- Benefit or curiosity-driven
- 40-60 characters ideal
- Test emoji (they're polarizing)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 354 lines · 130 tokens per session scan A 14b1248def7c
email-sequence is a skill published in the GitHub repository elkadrinaoufal1996/hormozi-marketing-ultimate (12 stars, last pushed 5mo ago), licensed MIT. It adds 130 tokens to every session and 2,702 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to emails, differing in 56 lines, and is treated as a copy.
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…