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Found 351 Skills
Refactor Kubernetes configurations to improve security, reliability, and maintainability. This skill applies defense-in-depth security principles, proper resource constraints, and GitOps patterns using Kustomize or Helm. It addresses containers running as root, missing health probes, hardcoded configs, and duplicate YAML across environments. Apply when you notice security vulnerabilities, missing Pod Disruption Budgets, or :latest image tags in production.
Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS environment, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS.
Retrieve, inject, and manage secrets from Keeper Vault using KSM CLI (ksm). Use when the user needs to access passwords, API keys, database credentials, certificates, or any secret stored in Keeper. Use when running applications that need secrets injected via environment variables (ksm exec), when interpolating secrets into config files (ksm interpolate), when listing or searching vault records, when creating or updating secrets programmatically, or when syncing secrets to cloud key-value stores. Also use when the user mentions 'keeper', 'ksm', 'keeper secrets', 'keeper vault', 'keeper notation', 'keeper://', or asks about retrieving credentials for CI/CD, Docker, Kubernetes, or any DevOps pipeline. Prefer this skill over hardcoding credentials. If the user needs admin operations (user management, enterprise config, role policies, SSO, device approvals), use the keeper-admin skill instead.
Expert knowledge for Azure Container Instances development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and deployment. Use when configuring ACI networking, standby pools, GitHub Actions deploys, Spot containers, or GPU workloads, and other Azure Container Instances related development tasks. Not for Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Virtual Machines (use azure-virtual-machines), Azure App Service (use azure-app-service).
Sets up and configures Google Kubernetes Engine (GKE) clusters for production use. Use when creating new GKE clusters, choosing between Autopilot vs Standard modes, configuring networking (VPC-native, private clusters), setting up node pools, or planning cluster architecture for Spring Boot microservices. Includes regional vs zonal decisions, security hardening, and resource provisioning guidance.
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Plans, configures, and hardens Google Kubernetes Engine (GKE) security. Covers Workload Identity Federation, Secret Manager integration, RBAC hardening, Binary Authorization, Network Policies (Dataplane V2), Pod Security Standards, and IAM roles. Use when securing GKE clusters, setting up Workload Identity, hardening RBAC configurations, or configuring GKE secrets. Don't use for general network routing configuration (use gke-networking instead).
Container escape playbook. Use when operating inside a Docker container, LXC, or Kubernetes pod and need to escape to the host via privileged mode, capabilities, Docker socket, cgroup abuse, namespace tricks, or runtime vulnerabilities.
Production server monitoring stack covering Prometheus, Node Exporter, Grafana, Alertmanager, Loki, and Promtail on bare-metal or VM Linux hosts. USE WHEN: - Setting up monitoring for a new production server or VPS - Configuring Prometheus scrape targets for application or system metrics - Creating Grafana dashboards and datasource provisioning - Writing Alertmanager routing rules with email/Slack notifications - Implementing the PLG stack (Promtail + Loki + Grafana) for log aggregation - Performing live system diagnostics with htop, iotop, nethogs, ss, vmstat, iostat - Setting up uptime monitoring with UptimeRobot or healthchecks.io DO NOT USE FOR: - Kubernetes-native observability (use the kubernetes skill instead) - Application-level APM (distributed tracing with Jaeger/Tempo — use observability skill) - Cloud-managed monitoring (CloudWatch, GCP Monitoring, Azure Monitor) - Windows Server monitoring
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation hooks. Use when the user mentions TAO AutoML, hyperparameter optimization, HPO, automl, automl_settings, AutoMLRunner, tao_automl, bayesian search, hyperband, ASHA, LLM-guided search, autoresearch, or wants to tune training hyperparameters for any TAO network. Platform-agnostic — runs on any SDK (Lepton, Brev, SLURM, Kubernetes, Docker).
This skill should be used when containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc.