Skill UI

Provides comprehensive monitoring for the Clay data enrichment pipeline. It tracks critical operational metrics such as credit consumption velocity, enrichment success rates (hit rates), data quality scores, and lead distribution. Essential for cost control and ensuring pipeline health in data-intensive CRM workflows.

A comprehensive system for implementing policy guardrails in data enrichment pipelines. It enforces critical controls including credit spending limits, blocks the enrichment of sensitive PII (e.g., SSN, bank details), and validates input data quality. Essential for maintaining compliance (GDPR/CCPA) and managing costs when processing large volumes of personal and business data.

This comprehensive checklist guides users through validating and preparing Clay enrichment pipelines for a production launch. It covers critical areas including table schema finalization, data quality gates (validation, deduplication), credit cost management, webhook reliability testing, and establishing robust monitoring and alerting procedures before going live.

Generates Python code and workflows for collaborative, content-based, or hybrid recommendation models, handling data prep, training, evaluation, and scalability so Claude can deliver tailored suggestions across domains.

Guides users through compression optimizer scenarios within data pipelines, offering best practices, code templates, and validation steps for ETL, transformation, and streaming workflows.

Automates cross validation setup for ML training requests, offering best-practice guidance, production-ready code samples, and validation steps across data preparation, model tuning, and experiment tracking.

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

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 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 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 guide provides comprehensive, code-based solutions for diagnosing and fixing common errors encountered in Databricks environments. It covers critical issues such as cluster state failures, Spark OutOfMemory errors, Delta Lake concurrency conflicts, permission problems, and schema mismatches, offering tailored fixes in Python, SQL, and Bash.

This comprehensive diagnostic bundle collects critical evidence from the Databricks environment, including environment setup, cluster state, job run history, and Delta table diagnostics. It generates a redacted, safe tar.gz file specifically designed for submitting detailed support tickets and efficiently troubleshooting complex data pipeline issues.