Skill UI

This guide details the comprehensive process of onboarding new log sources into Security Information and Event Management (SIEM) platforms. It covers critical steps including data source discovery, configuring collectors (e.g., Syslog, Splunk UF, CloudTrail), building parsers, normalizing fields to a common schema, and validating data quality. This systematic approach is essential for achieving complete security visibility and effective threat detection across the enterprise.

Generates HTML5 Canvas-based breadboard mockups and diagrams for retro computing builds, showing component placement, wiring, and labeled 6502/555 timer layouts as visual references or teaching aids.

A comprehensive guide to optimizing CAST AI's core performance metrics, including node provisioning speed, autoscaler responsiveness, and API call efficiency. This skill covers techniques like configuring headroom, selecting optimal instance families, tuning evictors, and implementing caching for multi-cluster dashboards to ensure high availability and cost efficiency in cloud environments.

This skill provides comprehensive API calls using `curl` to interact with the CAST AI platform. It allows users to list connected Kubernetes clusters, retrieve detailed cost savings reports, and inspect the inventory of managed nodes. It is essential for verifying connectivity, auditing cloud spending, and monitoring resource allocation after onboarding.

This comprehensive guide details how to set up end-to-end observability for ClickHouse. It covers extracting key metrics using built-in system tables (e.g., query logs, merge status), integrating the metrics with Prometheus, and finally visualizing performance, throughput, and health indicators on Grafana dashboards. Essential for monitoring production-grade ClickHouse deployments and configuring reliable alerting.

This skill provides a deep dive into the architecture of low-level keyloggers. It covers techniques such as kernel driver hooks, using WH_KEYBOARD_LL, ETW-based input capture, and analyzing user-mode vs. kernel-mode approaches. Use this methodology to understand input capture mechanisms, research EDR evasion methods, or analyze malware architecture for advanced security threat detection.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

Automates end-to-end provisioning for freshly plugged M5Stack or ESP32 boards: detect the device, flash UIFlow 2.0 firmware, and install a MicroPython app bundle. Useful for first-time setup, reflashing, app refresh, and smoke testing.