Skill UI

Step-by-step guidance for creating production-ready Kubernetes manifests including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Apply best practices, resource limits, health checks, and security contexts for multi-environment deployments.

A specialized workflow for deploying applications to Kubernetes, covering container orchestration, Helm charts, service mesh configuration, production-ready K8s patterns, security policies, and observability. Use for cluster deployments, chart creation, mesh setup, networking, and security hardening.

Deploy, scale, and manage Kubernetes workloads with YAML examples for Deployments, Services, Ingress, ConfigMaps, Secrets, HPA, storage, and kubectl debugging. Suitable for DevOps engineers and developers operating containerized applications.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

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.

Expert skill for building production-grade AI systems using LangChain 0.1+ and LangGraph. Covers agent architecture, RAG pipelines, memory systems, and deployment strategies. Ideal for developing sophisticated LLM applications with observability and scalability.

This skill covers setting up infrastructure for fine-tuning Large Language Models (LLMs) using techniques like QLoRA, LoRA, and full fine-tuning. It includes configuring distributed training with DeepSpeed or FSDP, utilizing frameworks such as Hugging Face TRL and Axolotl, and implementing alignment methods like RLHF and DPO. Suitable for domain-specific model adaptation and production deployment.

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

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

Loki Mode is a multi-agent autonomous startup system designed to execute workflows from PRD to production without human intervention. It integrates research-enhanced AI models including OpenAI SDK, DeepMind, Anthropic, and AWS Bedrock. This skill ensures zero human involvement in the deployment process while maintaining strict constraints on dependencies and response times.

Luna is an automated code reviewer focused on correctness, security, and reliability. She evaluates code against blueprints and checklists, identifying critical vulnerabilities like injection flaws, authentication bypasses, and race conditions. Luna acts as a quality gate, ensuring no unresolved high-risk issues proceed to QA or deployment. She excludes style preferences, focusing only on measurable risks to maintainability and safety.

Design and implement production-ready ML pipelines using multi-agent MLOps orchestration, covering data engineering, feature engineering, model training, experiment tracking, deployment, and serving.