Skill UI

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.

Step-by-step process for automating Dailybot tasks through Composio’s Rube MCP interface, covering connection setup, tool discovery, execution patterns, and schema compliance safeguards.

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

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

Automates guidance for data catalog updater tasks, covering ETL, transformation, orchestration, and streaming workflows to keep pipeline metadata accurate.

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.

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

Offers automated guidance for implementing data lineage trackers across ETL and streaming pipelines, including best practices on transformations, orchestration, and validation for production environments.

Automated guidance for data normalization tool tasks in ML training, offering best practices, production-ready code/configuration generation, and output validation.

Guides engineers through data partitioner setups within ETL workflows, offering best practice advice, ready-to-run code, and validation for streaming and batch pipelines.

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.

Automates the data story outlining process for analytic workflows, offering guidance on SQL, visualization, statistics, and BI so teams can structure insights consistently.