Skill UI

This skill provides step-by-step guidance for creating production-ready Kubernetes manifests. It covers essential resources including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Use this skill when defining complete, robust containerized application architectures, managing network connectivity, stateful workloads, and ensuring adherence to Kubernetes best practices like resource limits and security contexts.

Guides implementing Kubernetes NetworkPolicy, RBAC, PodSecurityPolicy, Pod Security Standards, and OPA Gatekeeper constraints to enforce defense-in-depth across clusters, covering multi-tenant protection, admission control, and Istio service mesh policies.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.

Automated guidance for Kubernetes service management in the DevOps Advanced domain, offering best-practice advice, code snippets, and config checks for services, Terraform, Helm, and monitoring workflows.

This guide provides production-grade architecture patterns for implementing robust LLM observability using Langfuse. It covers singleton SDK setup, context propagation across asynchronous boundaries, Express middleware for automatic tracing, and cross-service trace correlation using OpenTelemetry. Ideal for designing enterprise-level, scalable tracing infrastructure for LLM applications.

This skill provides comprehensive guidance for senior-level Laravel engineering. It focuses on building production-grade, maintainable, and highly secure applications using modern architectural patterns (clean architecture, services, etc.). Use this when developing new features, refactoring complex code, designing robust APIs, or optimizing database interactions within a Laravel environment. Adheres to best practices like Eloquent optimization and proper authentication flows.

This guide provides the framework for configuring Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for Lindy AI agent integrations. It covers setting up automated tests for webhook receivers, validating payload processing, and checking authentication signatures using GitHub Actions and Node.js, ensuring robust and reliable deployment of agent services.

A comprehensive guide for setting up a local development loop to test AI agent integrations. This skill teaches developers how to build and debug webhook receivers, callback handlers, and associated application code that interacts with a cloud-hosted AI agent (like Lindy). It requires using tunneling services (e.g., ngrok) to expose local endpoints for receiving webhooks and simulating the entire agent-to-application data flow.

This guide provides production-grade architectural patterns for integrating with the Linear API. It helps developers choose the optimal strategy—be it Simple SDK calls, Service-Oriented Gateways with caching, or Event-Driven Webhooks—based on system complexity, scale, and real-time requirements. Includes patterns like CQRS and rate limiting best practices.

A comprehensive guide to production-grade patterns for Linkerd, a lightweight, security-first service mesh for Kubernetes. This resource covers essential concepts like automatic mTLS, advanced traffic routing (e.g., canary deployments, A/B testing), service resilience using retries/timeouts, and robust access control policies, enabling reliable microservices architecture deployment.

A systematic, multi-phase workflow designed for diagnosing and resolving complex Linux system issues. It guides users through initial assessment, resource analysis (CPU, memory, disk), process investigation, log analysis, network diagnostics, and service failure resolution. Ideal for system administrators and DevOps engineers handling production incidents.