Skill UI

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

This skill automatically collects raw activity data (merged PRs, issues, commits, and optional Slack updates) from repositories. It formats this data using predefined templates, runs quality checks, and prepares a structured changelog draft. It can automatically commit the draft to a new branch and open a Pull Request (PR) for review, making it ideal for generating weekly or monthly release notes.

This skill provides comprehensive guidance on leveraging d3.js (Data-Driven Documents) to create sophisticated, interactive, and publication-quality data visualizations. It covers core concepts like data binding, implementing various chart types (network graphs, geo-maps, etc.), handling complex transitions, and ensuring responsiveness across modern JavaScript frameworks (React, Vue, Vanilla JS). Learn the standard workflow for scale definition, axis creation, and drawing custom visual elements with fine-grained control.

This comprehensive guide provides solutions for the most frequent errors encountered when using Clay for data enrichment, webhooks, and API integrations. It covers issues such as 422/404 webhooks, credit limits, data quality failures, and rate limiting, ensuring smooth data workflow management.

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.

A comprehensive workflow designed to orchestrate the entire lifecycle of large-scale features, from initial requirement gathering and architectural design through multi-service implementation, rigorous testing, and modern deployment strategies (e.g., canary, blue-green). Use this skill when coordinating complex, cross-functional feature delivery across frontend, backend, and data teams, ensuring quality and consistency at every phase.

Automates CodeQL database builds, suite selection, and taint-aware scans to surface security and quality issues across supported languages, handling output directories, SARIF results, and extensions in one workflow.

This comprehensive template provides a production-grade reference architecture for CodeRabbit AI code reviews. It is designed to guide teams in establishing rigorous coding standards by defining specific, best-practice instructions for various layers (API, Database, Authentication, Frontend, CI/CD, Infrastructure). Use this as a robust starting point to build a comprehensive, automated, and secure code quality review process.

Expert in designing, building, and optimizing scalable data pipelines, modern data warehouses, and real-time streaming architectures. Mastery covers the complete modern data stack, including Spark, dbt, Airflow, and cloud services (AWS, Azure, GCP). Focuses on ELT/ETL, data lakehouse implementation (Delta Lake, Iceberg), data governance, and ensuring high data quality and operational reliability.