Skill UI

A comprehensive runbook for standardizing incident response to Deepgram production issues. It guides users through automated triage (checking status, API connectivity, and model performance), severity classification (SEV1-SEV4), and implementing mitigation strategies, including building fallback services and replaying failed requests to ensure business continuity during outages or degradation.

A comprehensive collection of patterns and best practices for automating GitHub workflows using advanced AI models. This skill enables automated PR reviews, sophisticated issue triage (labeling and commenting), and seamless integration of AI into CI/CD pipelines, significantly enhancing DevOps efficiency and code quality assurance.

This comprehensive runbook guides incident response for Groq API failures. It covers triage steps, severity classification (P1-P4), troubleshooting decision trees (e.g., handling 429 errors or 5xx outages), and implementing immediate mitigations like model fallback or key rotation. Use this when responding to Groq outages to restore service stability.

This comprehensive skill profile models an expert network engineer specializing in modern cloud networking, security architectures, and performance optimization. It covers multi-cloud environments (AWS, Azure, GCP), advanced concepts like Zero Trust, Service Mesh (Istio), and high-availability design. Key capabilities include implementing IaC (Terraform), load balancing, DNS management, and deep network troubleshooting. Ideal for complex DevOps, SRE, or infrastructure design roles.

A comprehensive guide to optimizing cloud expenditure on the Vercel platform. It details how to understand the Fluid Compute pricing model, identify key cost drivers (such as function execution time, bandwidth, and middleware invocations), and implement best practices like edge caching, memory right-sizing, and aggressive asset caching. Use this when analyzing high Vercel billing, implementing cost controls, or managing a SaaS budget.

This skill implements the CISA Zero Trust Maturity Model (ZTMM) v2.0. It provides a structured framework to assess, gap-analyze, and roadmap the adoption of zero trust architecture across five core pillars: Identity, Devices, Networks, Applications, and Data. It guides organizations from Traditional to Optimal maturity, aiding compliance with NIST standards and federal mandates.

This guide details the design and implementation of security zones and conduits for Industrial Automation and Control Systems (IACS) following the stringent standards of IEC 62443-3-2. It enables risk-based network partitioning, assigning Security Level Targets (SL-T), and enforcing granular conduit controls. Use this when retrofitting flat OT networks or designing greenfield facilities to achieve IEC 62443 compliance, validating boundaries against the Purdue Reference Model.

This skill guides the implementation of fine-grained network security policies in Kubernetes using Calico CNI. It covers establishing a default-deny baseline, understanding policy ordering, and applying advanced rules like service-account-based selectors. Use it for hardening cluster security, enforcing zero-trust models, and managing network segmentation within containerized environments.

Conduct comprehensive security assessments for Operational Technology (OT) networks, including SCADA and DCS systems. This skill maps industrial protocols (Modbus, DNP3, OPC UA) and analyzes traffic against the Purdue Model. It is crucial for identifying IT/OT convergence risks, detecting misconfigurations, and ensuring compliance with standards like IEC 62443.

This advanced code review skill forces genuine perspective shifts by employing three hostile reviewer personas: the Saboteur (production breaks), the New Hire (maintainability), and the Security Auditor (OWASP-informed). It ensures that all code reviews are critically rigorous, eliminating superficial "Looks Good To Me" approvals and catching blind spots missed by the original author or general AI models. Use it before merging a PR for maximum confidence.

This skill provides comprehensive guidance for deploying and managing GPU-accelerated AI inference services on CoreWeave Kubernetes (CKS). It covers best practices including containerization using NVIDIA CUDA base images, configuring specific GPU resource limits (A100/H100), setting up robust health checks, and executing controlled rolling updates. Ideal for managing multi-model inference and scaling demanding AI workloads in a cloud environment.

This runbook provides structured steps for responding to critical production incidents on the CoreWeave platform. Use it when dealing with GPU workload failures, inference service outages, or general Kubernetes resource issues. It guides users through checking pod status, node health, and diagnosing common model loading errors to ensure rapid service recovery.