email-sequence
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
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
More from silvainfm/claude-skills
duckdb
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
232streamlit
Fast Python framework for building interactive web apps, dashboards, and data visualizations without HTML/CSS/JavaScript. Use when user wants to create data apps, ML demos, dashboards, data exploration tools, or interactive visualizations. Transforms Python scripts into web apps in minutes with automatic UI updates.
198reflex-dev
Guide for building full-stack web applications using Reflex, a Python framework that compiles to React frontend and FastAPI backend. Use when creating, modifying, or debugging Reflex apps - covers state management, event handlers, components, routing, styling, and data integration patterns.
92project-planner
Comprehensive project planning and documentation generator for software projects. Creates structured requirements documents, system design documents, and task breakdown plans with implementation tracking. Use when starting a new project, defining specifications, creating technical designs, or breaking down complex systems into implementable tasks. Supports user story format, acceptance criteria, component design, API specifications, and hierarchical task decomposition with requirement traceability.
38polars
Lightning-fast DataFrame library written in Rust for high-performance data manipulation and analysis. Use when user wants blazing fast data transformations, working with large datasets, lazy evaluation pipelines, or needs better performance than pandas. Ideal for ETL, data wrangling, aggregations, joins, and reading/writing CSV, Parquet, JSON files.
29reportlab
Python library for programmatic PDF generation and creation. Use when user wants to generate PDFs from scratch, create invoices, reports, certificates, labels, or custom documents with precise control over layout, fonts, graphics, tables, and charts. Supports both low-level drawing (Canvas) and high-level documents (Platypus).
23