Skill UI

This SDK provides comprehensive support for automatically instrumenting Node.js applications to collect observability data. It integrates distributed tracing, metrics, and logs using the OpenTelemetry standard, allowing seamless data export and monitoring within Azure Monitor. It supports various resource types and connection methods.

This SDK provides a comprehensive Java client for interacting with Azure Monitor. It allows developers to execute powerful Kusto Query Language (KQL) queries against Log Analytics workspaces to retrieve detailed operational logs, and to query time-series metrics from various Azure resources. It supports both synchronous and asynchronous operations, making it ideal for building monitoring, auditing, and analytics features in Java applications.

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.

This comprehensive framework allows users to validate and test both crypto and traditional trading strategies against extensive historical market data. It calculates essential performance metrics (Sharpe Ratio, Sortino Ratio, Max Drawdown), simulates trade execution, generates equity curves, and includes advanced parameter grid search optimization tools for robust strategy validation.

Automates building and evaluating classification models by analyzing datasets, selecting features, tuning hyperparameters, and reporting metrics via the classification-model-builder plugin for supervised learning requests.

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.

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

A comprehensive expert system for analyzing software codebases to identify, quantify, and prioritize technical debt across various dimensions (code, architecture, testing, infrastructure). It provides detailed metrics, impact assessments (development velocity, quality loss), and actionable, prioritized remediation roadmaps with clear ROI projections.

Monitor the effectiveness and adoption of AI code reviews using CodeRabbit. This skill calculates critical metrics including review coverage percentage, time-to-first-review, and comment acceptance rates. It is ideal for building organizational dashboards to maintain high code quality and optimize development workflows.

This comprehensive guide details how to implement robust webhook endpoints for CodeRabbit, an AI-powered code review tool. It covers essential security practices, including signature validation (HMAC-SHA256), and provides structured methods for handling various event types (e.g., PR reviews, check run completions, comments). Use this skill when integrating CodeRabbit into CI/CD pipelines to automate notifications, track metrics, and enforce quality gates.

Automates configuring and aggregating infrastructure metrics across compute, storage, network, containers, load balancers, and databases with Prometheus, Datadog, or CloudWatch for dashboards and capacity tracking.

Maps architectural components within a codebase, computes statement-based size metrics, flags oversized or undersized modules, and generates an inventory to guide decomposition planning.