memstack-content-email-sequence

An email sequence is a series of planned messages sent as part of an automated campaign, such as onboarding, launch, or customer follow-up. This skill creates the subject lines, preview text, message content, calls to action, and testing suggestions for the series.

In plain words
What is it for?
Use it to create onboarding emails, launch emails, drip campaigns, nurture sequences, and other multi-email campaigns.
Why use it?
It helps plan several related emails as one journey instead of writing disconnected messages. It also separates automated campaigns from one-off emails and recurring newsletters.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/cwinvestments/memstack/email-sequence
Any agent
npx skills add cwinvestments/memstack --skill email-sequence
Clone the repo
git clone --depth 1 https://github.com/cwinvestments/memstack

Made for: Claude Code, Codex.

Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,562 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00066 $0.03562
Opus 5 $0.00033 $0.01781
Sonnet 5 $0.00013 $0.00712
Haiku 4.5 $0.00007 $0.00356

Measured 2d ago against content hash 9f097840fa62, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

memstack-content-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 2d 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.

skills/content/email-sequence/SKILL.md · 441 lines

How it starts

The opening of the file, as written. The whole thing — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.

📧 Email Sequence — Writing automated email campaign...

Produces a complete multi-email sequence with subject lines, preview text, body copy, CTAs, and A/B test suggestions — ready to load into any email platform.

Activation

When this skill activates, output:

📧 Email Sequence — Designing email campaign structure...

Then execute the protocol below.

Context Status
User says "write email sequence" or "email series" or "drip campaign" ACTIVE
User says "nurture sequence" or "onboarding emails" or "launch emails" ACTIVE
Creating a multi-email automated campaign ACTIVE
Writing a single one-off email DORMANT — just write the email directly
Writing newsletter content DORMANT
Writing email for support or personal communication DORMANT

Anti-patterns

Trap Reality Check
"Pitch in the first email" Email 1 is for trust, not selling. Premature pitching gets unsubscribes, not conversions.
"More emails = more sales" Frequency breeds fatigue. 5 focused emails beat 12 filler emails. Quality per email matters more than quantity.
"Long subject lines explain more" Subject lines are read on mobile. After 50 characters, they're truncated. Short + curious wins.
"The body copy needs to be short" Length doesn't kill — boring does. A 500-word email that's engaging outperforms a 100-word email that's bland.
"Everyone gets the same sequence" Segmentation doubles conversion. At minimum, separate new subscribers from existing customers.

Protocol

Step 1: Gather Campaign Details

If the user hasn't provided details, ask:

I need a few details for the email sequence:

  1. Product/service — what are you promoting or onboarding for?
  2. Target audience — who receives these emails? (new signups, leads, customers, etc.)
  3. Sequence goal — what's the purpose?
    • Nurture (build trust over time)
    • Launch (build anticipation, sell on launch day)
    • Onboarding (activate new users)
    • Re-engagement (win back inactive users)
  4. Entry trigger — what puts someone into this sequence? (signup, purchase, abandoned cart, etc.)
  5. Existing relationship — do they already know you, or is this first contact?

Read the full file on GitHub · 441 lines

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. 2d ago First seen · 441 lines · 66 tokens per session scan A 9f097840fa62

Subscribe to this mod's changes

memstack-content-email-sequence is a skill published in the GitHub repository cwinvestments/memstack (417 stars, last pushed 6d ago), licensed MIT. It adds 66 tokens to every session and 3,562 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 2026-08-30.

Related

Other skills, from other repositories

frontend-design-landing-page

Marketing landing page and conversion-focused product page reference. Use this skill when building hero sections, feature grids, pricing pages, testimonials, CTAs, footers, navigation bars, or any public-facing marketing surface. Covers a warm, professional, developer-friendly design language (cream backgrounds…

cloudflare/vibesdk · 106 tokens

plan-ceo-review

CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Four modes: SCOPE EXPANSION (dream big), SELECTIVE EXPANSION (hold scope + cherry-pick expansions), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials). Use when…

GCWing/BitFun · 143 tokens

ship

Ship workflow: detect + merge base branch, run tests, review diff, bump VERSION, update CHANGELOG, commit, push, and create a PR. Use for an explicit /ship invocation or when the user requests the full ship, release, or deploy workflow. For an ordinary commit, push, or pull-request publishing request, use the built-in…

GCWing/BitFun · 88 tokens

design-review

Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them. Iteratively fixes issues in source code, committing each fix atomically and re-verifying with before/after screenshots. For plan-mode design review (before implementation), use…

GCWing/BitFun · 125 tokens

plan-eng-review

Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations. Use when asked to "review the architecture", "engineering review", or "lock in the plan". Proactively suggest when…

GCWing/BitFun · 116 tokens

autoplan

Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. Surfaces taste decisions (close approaches, borderline scope, codex disagreements) at a final approval gate. One command, fully reviewed plan out. Use when…

GCWing/BitFun · 152 tokens