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.

This comprehensive skill simulates an expert business analyst, specializing in transforming complex raw data into actionable, strategic insights. The scope covers modern Business Intelligence (BI) platforms (Tableau, Power BI), AI-powered predictive modeling, designing robust KPI/OKR frameworks, advanced financial modeling, and optimizing business processes. It is designed for users needing to drive data-driven decision-making and create executive-level reports.

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.

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

Transforms raw datasets into charts, plots, and graphs by analyzing structure, picking suitable visualization types, and rendering them clearly so users can request visualizations or explore patterns and relationships.

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.

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.

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.

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