Skill UI

This skill covers implementing service mesh patterns for AI inference traffic management using Istio or Linkerd. It includes configuring mTLS for security, setting up authorization policies for service accounts, managing egress for model endpoints, and implementing traffic splitting for canary releases and A/B testing. It ensures cross-cluster resilience and protects GPU-backed services from cascading failures without modifying application code.

Learn to implement zero-downtime deployment strategies for production systems. This skill covers blue-green, canary, and rolling deployments using Kubernetes, AWS ECS, and Istio. Includes configuration for load balancers, health checks, and traffic switching scripts to ensure seamless updates without service interruption.

A command-line utility to search and extract data from Burp Suite project files (.burp). It enables searching response headers or bodies with regex patterns, extracting security audit findings, and analyzing HTTP traffic. Requires Burp Suite Professional and a specific extension. Ideal for security auditing and traffic analysis without opening the GUI.

This skill covers configuring host-based and cloud firewalls using iptables, nftables, and UFW. It includes implementing network segmentation, traffic filtering, and security zone policies. Suitable for securing server perimeters, migrating firewall technologies, and responding to security incidents. Requires Linux root access and network knowledge.

Comprehensive guide to Istio traffic management for production service mesh deployments, covering routing, canary deployments, circuit breakers, retries, traffic mirroring, fault injection, and load balancing strategies.

This skill covers production patterns for Linkerd, a lightweight security-first service mesh for Kubernetes. It includes setup instructions, namespace injection, service profiles for retries/timeouts, traffic splits for canary deployments, server authorization policies, and multi-cluster configurations. Ideal for DevOps engineers managing microservices security and traffic management.

Deploy a unified API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

Configure load balancers and traffic distribution. Implement health checks and SSL termination. Use when distributing traffic across servers.

Deploy ML models on Kubernetes with KServe and NVIDIA Triton Inference Server. Covers canary deployments, autoscaling, GPU-aware scheduling, and traffic splitting for production inference workloads.

This skill enables authorized reverse engineering of custom binary protocols, Protobuf/gRPC schemas, and WebSocket frames using PCAP data. It helps recover undocumented wire protocols, decode structured traffic, and reconstruct state machines for security analysis and debugging purposes.

This specialized skill guides users through a structured, forensic investigation process when facing sudden, significant drops in organic traffic or search rankings. It is designed for incident scenarios—such as suspected penalties, Core Updates, major technical regressions, or unexplained traffic crashes—providing a systematic approach to triage, root-cause analysis, and developing a prioritized recovery plan.