Skill UI

Utilizes the Azure AI Projects SDK's ImageBasedHostedAgentDefinition to build robust, container-based AI agents deployed in Azure AI Foundry. This workflow is essential for creating production-grade agents that require custom logic, precise resource allocation (CPU/Memory), and integration of advanced tools like code interpreters.

Implement comprehensive observability for Apollo.io API integrations. This skill provides structured instrumentation covering Prometheus metrics (request count, latency, rate limits, credits used), structured logging with PII redaction, and OpenTelemetry tracing. It ensures reliable tracking of API health, performance, and resource consumption.

This comprehensive tool provides an automated, multi-stage checklist for deploying Apollo.io integrations to a production environment. It validates crucial aspects including secure authentication (API keys, headers), system resilience (rate limiting, retry logic), observability (PII redaction, structured logging), and financial controls (credit tracking). Use this checklist before going live or when performing production system audits to ensure full compliance and stability.

A comprehensive guide covering security best practices for integrating with the Apollo.io API. This includes secure key storage (using secret managers/env vars), implementing PII redaction for logging, adhering to the principle of least privilege (using standard vs. master keys), and performing key rotation procedures. Essential for building secure and compliant integrations.

A comprehensive SDK for Python to interact with Azure Event Grid. It enables developers to build robust, event-driven architectures (EDA) by providing mechanisms to publish standard CloudEvents and Azure-native EventGridEvents. Use it for implementing complex publish/subscribe patterns and integrating services within the Azure cloud ecosystem.

A comprehensive SDK for Python to interact with Azure Event Grid. It enables developers to build robust, event-driven architectures (EDA) by providing mechanisms to publish standard CloudEvents and Azure-native EventGridEvents. Use it for implementing complex publish/subscribe patterns and integrating services within the Azure cloud ecosystem.

A comprehensive guide on using the Azure Service Bus SDK in TypeScript for enterprise messaging. It demonstrates core asynchronous patterns, including sending/receiving messages from queues, publishing to topics with subscriptions, managing message sessions, and handling advanced features like dead-lettering and scheduled messages. Essential for building robust, scalable, event-driven microservices.

This guide details security best practices for integrating with Clay. It covers securing API keys, validating webhook signatures, isolating provider credentials, and redacting sensitive PII data. Essential for maintaining compliance and data integrity in production environments.

This skill provides a comprehensive solution for implementing observability for Customer.io integrations. It covers setting up Prometheus metrics (latency, request counts, bounces), structured JSON logging with PII redaction, and OpenTelemetry tracing. Use it when building robust, production-grade monitoring dashboards and alerting systems for Customer.io APIs.

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 comprehensive guide outlines the end-to-end process of building production-grade ETL data pipelines using Databricks and Delta Lake. It implements the robust Medallion Architecture (Bronze, Silver, Gold) for scalable data management. Techniques covered include Auto Loader for incremental streaming ingestion, MERGE INTO for upsert semantics, data cleansing, and advanced aggregation into analytics-ready layers. Ideal for establishing reliable data pipelines in a data lakehouse environment.

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.