Skill UI

Automates cohort analysis creation by offering step-by-step analytics guidance, SQL patterns, visualization advice, and validation so analysts can produce production-ready insights within data analytics workflows.

Provides automated, industry-standard guidance and code for complex SQL joins, visualizations, and BI analyses whenever the “complex join helper” skill is invoked in a data analytics context.

This expert guides users through advanced, data-driven content marketing strategies. It covers AI-powered content creation, comprehensive SEO optimization, omnichannel distribution, social media management, email automation, and performance analytics. Use it when developing comprehensive content plans, maximizing online reach, or optimizing conversion funnels across various digital platforms.

Automates correlation analyzer workflows by offering best-practice guidance, executable SQL, visualization tips, and statistical validation for data analytics requests.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

Automates dashboard layout planning for data analytics tasks, guiding best practices, generating robust configs, and validating layouts for SQL-driven BI scenarios.

Guides data engineering patterns for ETL pipelines, Spark analytics, and quality checks, showcasing Python and SQL snippets to extract, transform, load, validate, and model sales data in modern warehouses.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

This comprehensive guide outlines the end-to-end process of building production-grade ETL data pipelines using Databricks and Delta Lake. It implements the robust Medallion Architecture (Bronze, Silver, Gold) for scalable data management. Techniques covered include Auto Loader for incremental streaming ingestion, MERGE INTO for upsert semantics, data cleansing, and advanced aggregation into analytics-ready layers. Ideal for establishing reliable data pipelines in a data lakehouse environment.

Automates guidance for date range analytics, covering SQL, visualization, statistics, and BI, with code generation and validation to support data analytics workflows.

This advanced workflow provides comprehensive meeting intelligence by allowing users to search across stored transcripts using keywords and date filters. It integrates the AskFred AI assistant for natural language Q&A over single or cross-meeting data. Furthermore, it aggregates detailed meeting analytics, providing key metrics such as total duration, action items, and top keywords for robust reporting and knowledge retrieval.

Provides a robust, event-driven reference architecture for building meeting intelligence platforms using Fireflies.ai. This guide details the full production pipeline, covering multi-source meeting ingestion (Zoom, Teams), webhook processing, and subsequent data enrichment into structured action items, analytics reports, and database storage. Ideal for designing complex integrations or advanced meeting analytics systems.