Skill UI

This skill provides comprehensive operational controls for long-lived, cloud-hosted agent workloads. It manages the entire lifecycle—from deployment and scaling to observability and security. Features include detailed monitoring (logs, metrics, traces), safety controls (kill switches, least privilege), and robust change management processes (rollout, rollback, auditing). Ideal for mission-critical microservices and background agents.

A comprehensive collection of 37 advanced engineering skills for complex architecture, automation, reliability, and platform operations. Includes agent design, CI/CD pipelines, database management, observability, and security auditing. Use these skills to select the right tool for specific engineering tasks involving system design, deployment, and maintenance.

This skill guides the setup of a robust, centralized logging pipeline using Fluent Bit and Fluentd. It covers collecting logs from diverse sources (syslog, files, applications), routing, filtering, and enriching them before sending them to destinations like Elasticsearch, S3, or Splunk. Ideal for centralizing observability and building secure logging architectures across distributed infrastructure.

Tetragon utilizes eBPF technology to provide deep, low-overhead security observability and runtime enforcement directly within Kubernetes clusters. It monitors system calls, process execution, file access, and network connections at the kernel level, allowing users to detect or block dangerous activities like container escapes or privilege escalation, ensuring robust cluster security and compliance.

A comprehensive guide on implementing advanced security observability using eBPF and Cilium Tetragon. This skill covers real-time tracking of process execution, network connections, and file system access directly in the kernel. Learn how to author TracingPolicy CRDs, utilize kprobe/tracepoint hooks for in-kernel filtering, and enforce runtime security policies (e.g., process killing) on Linux hosts or Kubernetes clusters.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

This skill provides comprehensive best practices for monitoring Go services in production. It covers the five critical pillars of observability: structured logging (slog), Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling, and Real User Monitoring (RUM). Use this skill to proactively instrument new features, set up advanced alerting, correlate logs with traces, and ensure full visibility into system health and user experience.

This skill covers preparing for production launches, including pre-launch checklists for code quality, security, and performance. It details feature flag strategies, staged rollout sequences, and rollback conditions. It also emphasizes monitoring and observability to ensure safe deployments with clear success metrics.

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.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.