skills/marketing/campaigns-and-ideas/marketingskills/email-sequence/SKILL.md
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. For cold outreach emails, see cold-email. For in-app onboarding, see onboarding-cro.
npx skillsauth add lunartech-x/superpowers email-sequenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
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
Audience Context
Goals
Depends on:
Consider:
Patterns that work:
Length: 5-7 emails over 12-14 days Goal: Activate, build trust, convert
Key emails:
Length: 6-8 emails over 2-3 weeks Goal: Build trust, demonstrate expertise, convert
Key emails:
Length: 3-4 emails over 2 weeks Trigger: 30-60 days of inactivity Goal: Win back or clean list
Key emails:
Length: 5-7 emails over 14 days Goal: Activate, drive to aha moment, upgrade Note: Coordinate with in-app onboarding—email supports, doesn't duplicate
Key emails:
For detailed templates: See references/sequence-templates.md
For detailed email type reference: See references/email-types.md
For detailed copy, personalization, and testing guidelines: See references/copy-guidelines.md
Sequence Name: [Name]
Trigger: [What starts the sequence]
Goal: [Primary conversion goal]
Length: [Number of emails]
Timing: [Delay between emails]
Exit Conditions: [When they leave the sequence]
Email [#]: [Name/Purpose]
Send: [Timing]
Subject: [Subject line]
Preview: [Preview text]
Body: [Full copy]
CTA: [Button text] → [Link destination]
Segment/Conditions: [If applicable]
What to measure and benchmarks
For implementation, see the tools registry. Key email tools:
| Tool | Best For | MCP | Guide | |------|----------|:---:|-------| | Customer.io | Behavior-based automation | - | customer-io.md | | Mailchimp | SMB email marketing | ✓ | mailchimp.md | | Nitrosend | AI-native email (sequences via prompts) | ✓ | nitrosend.md | | Resend | Developer-friendly transactional | ✓ | resend.md | | SendGrid | Transactional email at scale | - | sendgrid.md | | Kit | Creator/newsletter focused | - | kit.md |
tools
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
testing
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
development
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
development
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.