Skill UI

This skill detects advanced lateral movement techniques used by attackers after initial compromise, mapping them to MITRE ATT&CK TA0008. It correlates multiple data sources, including Windows event logs (4624, 4769), Sysmon telemetry, network flow data, and endpoint logs, to identify suspicious activities like Pass-the-Hash, PsExec, WMI exploitation, RDP pivoting, and SMB spreading.

This skill detects stealthy lateral movement by monitoring the abuse of DCOM objects (MMC20.Application, ShellBrowserWindow, ShellWindows). It correlates Sysmon telemetry (Process Creation, Network Connections), WMI analysis, and RPC traffic to identify unauthorized remote command execution, aiding threat hunters and red teams.

A comprehensive skill for engineering high-quality, observable Go services. It guides best practices across five key signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling (pprof), and RUM. Use this when instrumenting new features, reviewing PRs, or auditing entire codebases to ensure systems are fully measurable for reliable diagnosis and alerting.

Guidelines for crafting industrial brutalist interfaces that merge Swiss print and military terminal aesthetics, emphasizing rigid grids, stark typography, utilitarian color, and analog degradation for data-heavy dashboards or editorial sites.

Guide to instrument Azure-hosted ASP.NET Core or Node.js web apps with telemetry reporting to Azure App Insights for improved observability and health tracking.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

This advanced workflow automates the process of analyzing the operational health of specific Azure resources. It ingests and analyzes raw logs, telemetry data, and performance metrics from diverse Azure services (e.g., VMs, Web Apps, Databases). The system performs comprehensive root cause analysis, classifies issues (Critical, High, Medium), and generates actionable, step-by-step remediation plans, significantly reducing manual troubleshooting time.

OpenSearch is a distributed search and analytics engine integrated into the KubeSphere WizTelemetry Observability Platform. It is crucial for storing, searching, and analyzing various types of observability data, including application logs, system events, audit records, and notification history. This guide covers installation, configuration, and operational best practices for managing the OpenSearch cluster within KubeSphere.

This service provides a centralized, common API server backend for the entire KubeSphere WizTelemetry Observability Platform. It unifies backend functionalities for core observability components including Logging, Auditing, Events, Tracing, and Notifications, enabling global monitoring and streamlined platform management within Kubernetes environments. Use it when installing or configuring the core telemetry platform.

WizTelemetry Logging is a core extension component within the KubeSphere Observability Platform, designed for comprehensive log collection, processing, and storage. It supports advanced log collection architectures, including collecting container stdout/stderr logs (via vector-logging) and collecting logs directly from the container filesystem (via logsidecar-injector). Use this guide to understand its configuration, dependencies, and how to query logs using the dedicated API.

A comprehensive guide to implementing observability for Klaviyo API integrations. This solution uses Prometheus metrics (counters, histograms, gauges) to track API volume, latency, and errors, combined with OpenTelemetry for distributed tracing and structured logging. Ideal for setting up robust monitoring dashboards and critical alerting systems for marketing data pipelines.

This guide details how to implement comprehensive observability for various Adobe API integrations (e.g., Firefly, PDF Services). It covers the four pillars of observability: structured metrics using Prometheus, distributed tracing via OpenTelemetry, structured JSON logging, and defining critical alert rules. This ensures deep visibility into API performance, latency, and error rates for robust production monitoring.