Skill UI

This comprehensive guide details essential security best practices for integrating with Customer.io. It covers secure credential storage using secrets managers, sanitizing Personally Identifiable Information (PII) via hashing, verifying webhook signatures (HMAC-SHA256) to ensure data integrity, and establishing procedures for API key rotation to maintain compliance with GDPR/CCPA regulations.

A robust solution for implementing and handling real-time webhooks from Customer.io. This tool processes critical marketing events, including delivery status, opens, clicks, bounces, and unsubscribes. Key features include HMAC signature verification for security, reliable asynchronous event processing, and structured routing to manage various customer interactions, allowing data to be streamed efficiently to a data warehouse or CRM.

Daily News Report orchestrates agents to scrape a curated URL list, filter high-quality technical findings, and compile them into daily Markdown summaries while monitoring batch progress and leveraging smart caching for reliability.

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.

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

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.

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.

This skill provides production patterns for implementing robust data quality validation using Great Expectations, dbt tests, and formal data contracts. It is essential for building reliable, automated, and verifiable data pipelines, ensuring data integrity throughout the entire ETL/ELT process, and establishing clear data governance rules.

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.

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