email-drip-sequence

email-drip-sequence is a skill for Claude Code, Codex from Gingg7260/affiliate-skills. It costs 106 tokens per session (2,982 once invoked), scanned A, a copy of email-drip-sequence, MIT.

A writing aid for planning a 5–7 email drip sequence, meaning a series of emails sent automatically over time to new subscribers. The sequence moves from welcome and useful information toward an affiliate product recommendation.

In plain words
What is it for?
Use it for welcome campaigns after a signup, product-focused email funnels, and automated affiliate promotions in services such as ConvertKit, Mailchimp, Beehiiv, or ActiveCampaign.
Why use it?
It turns a contact list into a planned follow-up series instead of requiring each email to be written from scratch. It also gives every email a subject line, preview text, message, and one clear next step.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; mentions Claude Code; built for openclaw.

Good fit Use it for welcome campaigns after a signup, product-focused email funnels, and automated affiliate promotions in services such as ConvertKit, Mailchimp, Beehiiv, or ActiveCampaign.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gingg7260/affiliate-skills/email-drip-sequence
Install

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.

Any agent
npx skills add Gingg7260/affiliate-skills --skill email-drip-sequence
Clone the repo
git clone --depth 1 https://github.com/Gingg7260/affiliate-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for email-drip-sequence

README.md
[![agentmods](https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/email-drip-sequence/github.svg)](https://agentmods.dev/skills/gingg7260/affiliate-skills/email-drip-sequence)
Your own site
<a href="https://agentmods.dev/skills/gingg7260/affiliate-skills/email-drip-sequence"><img src="https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/email-drip-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.

agentmods 80×15 button for email-drip-sequence

Your own site · 80×15
<a href="https://agentmods.dev/skills/gingg7260/affiliate-skills/email-drip-sequence"><img src="https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/email-drip-sequence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,982 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00106 $0.02982
Opus 5 $0.00053 $0.01491
Sonnet 5 $0.00021 $0.00596
Haiku 4.5 $0.00011 $0.00298

Measured 9d ago against content hash a5b8ba883e19, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

email-drip-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 9d 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.

Origin

This is a copy

100% identical to email-drip-sequence — 0 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.

skills/distribution/email-drip-sequence/SKILL.md · 267 lines

How it starts

The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Email Drip Sequence

Write a 5-7 email drip sequence that nurtures new subscribers from cold to warm to buyer. Follows the Welcome → Value → Value → Soft Sell → Hard Sell → Objection Handling → Follow-Up pattern. Each email includes subject line, preview text, body copy, and a single clear CTA.

Stage

S5: Distribution — Email is the highest-ROI channel for affiliate marketers (avg $42 return per $1 spent). This skill turns a list of subscribers into a predictable revenue stream by delivering value first and selling second.

When to Use

  • User has an email list and wants to promote an affiliate product
  • User just launched a lead magnet or opt-in form and needs a welcome sequence
  • User wants to automate affiliate promotions via email automation (ConvertKit, Mailchimp, Beehiiv, ActiveCampaign, etc.)
  • User says anything like "email sequence", "drip campaign", "email funnel", "nurture series"
  • User wants a sequence for a specific product or niche
  • Chaining from S1 (research) — user found a product and now wants an email sequence for it

Input Schema

product:
  name: string              # REQUIRED — product name (e.g., "HeyGen")
  affiliate_url: string     # REQUIRED — the affiliate link to promote
  category: string          # OPTIONAL — product category (e.g., "AI video tool")
  reward_value: string      # OPTIONAL — commission amount/percentage (e.g., "30% recurring")
  key_benefits: string[]    # OPTIONAL — top 3 benefits. Auto-researched if not provided.
  price: string             # OPTIONAL — product pricing (e.g., "$29/mo")

audience:
  description: string       # REQUIRED — who are the subscribers? (e.g., "content creators", "SaaS founders")
  pain_point: string        # OPTIONAL — main problem they want solved
  awareness_level: string   # OPTIONAL — "cold" | "warm" | "hot". Default: "cold"

sequence:
  length: number            # OPTIONAL — number of emails: 5, 6, or 7. Default: 7
  send_days: number[]       # OPTIONAL — days to send (e.g., [0, 1, 3, 5, 7, 10, 14])
                            # Default: [0, 1, 3, 5, 7, 10, 14]
  sender_name: string       # OPTIONAL — from name (e.g., "Alex from ContentPro")
  tone: string              # OPTIONAL — "conversational" | "professional" | "bold"
                            # Default: "conversational"
  lead_magnet: string       # OPTIONAL — what they opted in for (e.g., "AI tools checklist")

Read the full file on GitHub · 267 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 9d ago First seen · 267 lines · 106 tokens per session scan A a5b8ba883e19

Subscribe to this mod's changes

email-drip-sequence is a skill published in the GitHub repository Gingg7260/affiliate-skills (5 stars, last pushed 2d ago), licensed MIT. It adds 106 tokens to every session and 2,982 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to email-drip-sequence, differing in 0 lines, and is treated as a copy.

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