Over 160 production-ready skill documents (SKILL.md) for LLM coding agents like Claude Code, Cursor, and Codex.Each skill contains expert-level configs, CLI commands, troubleshooting guides, and ready-to-run scripts spanning six domains: DevOps (CI/CD, Kubernetes, observability), Security (vulnerability scanning, secrets, hardening), Infrastructure (AWS/Azure/GCP, networking, databases, local inference stacks), LLM engineering (agent evals, RAG infrastructure, inference scaling), Compliance (SOC2, HIPAA, GDPR, ISO 27001), and IT operations. Skills install in under a minute via the `npx skills add` CLI or a simple git clone, and the agent discovers, matches, and activates them on demand.
- Ships with notable niche skills absent from other repos: MCP server security, eBPF kernel-level observability, OpenTofu migration, agent-eval CI/CD gates, and multi-tenant LLM hosting with KEDA autoscaling.
- Ships with notable niche skills absent from other repos: MCP server security, eBPF kernel-level observability, OpenTofu migration, agent-eval CI/CD gates, and multi-tenant LLM hosting with KEDA autoscaling.
STCLab's SRE team shares their experience building an AI-driven investigation pipeline to automate the triage of Kubernetes alerts. By utilizing HolmesGPT, they implemented a ReAct pattern that allows LLMs to autonomously select tools like Prometheus, Loki, and kubectl based on specific context. The core finding was that high-quality markdown runbooks containing exclusion rules were more critical for successful investigations than the underlying AI model itself.
Key points:
* Implementation of HolmesGPT using the ReAct agent pattern for autonomous troubleshooting.
* Integration with Robusta to manage Slack routing, deduplication, and thread matching.
* The vital role of runbooks in narrowing search spaces and reducing wasted tool calls.
* Comparison between self-hosted models via KubeAI and managed API approaches.
* Significant reduction in manual triage time from 20 minutes to under two minutes per investigation.
Key points:
* Implementation of HolmesGPT using the ReAct agent pattern for autonomous troubleshooting.
* Integration with Robusta to manage Slack routing, deduplication, and thread matching.
* The vital role of runbooks in narrowing search spaces and reducing wasted tool calls.
* Comparison between self-hosted models via KubeAI and managed API approaches.
* Significant reduction in manual triage time from 20 minutes to under two minutes per investigation.
An effort to create a fully functional Kubernetes cluster with 1 million active nodes. The article details the challenges and solutions for scaling Kubernetes to this size, covering networking, state management (etcd), and the scheduler.
The Azure MCP Server implements the MCP specification to create a seamless connection between AI agents and Azure services. It allows agents to interact with various Azure services like AI Search, App Configuration, Cosmos DB, and more.
The article discusses the increasing complexity of Kubernetes and suggests that Silicon Valley is exploring alternative technologies for container orchestration, citing a benchmark showing a stripped-down stack outperforming Kubernetes.
Kagent is an open-source agentic AI framework for Kubernetes that aims to provide autonomous problem solving and remediation for cloud-native infrastructure, moving beyond traditional automation to a more intelligent and self-healing system.
Running GenAI models is easy. Scaling them to thousands of users, not so much. This guide details avenues for scaling AI workloads from proofs of concept to production-ready deployments, covering API integration, on-prem deployment considerations, hardware requirements, and tools like vLLM and Nvidia NIMs.
K8S-native cluster-wide deployment for vLLM. Provides a reference implementation for building an inference stack on top of vLLM, enabling scaling, monitoring, request routing, and KV cache offloading with easy cloud deployment.
vLLM Production Stack provides a reference implementation on how to build an inference stack on top of vLLM, allowing for scalable, monitored, and performant LLM deployments using Kubernetes and Helm.
A user is seeking advice on deploying a new server with 4x H100 GPUs (320GB VRAM) for on-premise AI workloads. They are considering a Kubernetes-based deployment with RKE2, Nvidia GPU Operator, and tools like vLLM, llama.cpp, and Litellm. They are also exploring the option of GPU pass-through with a hypervisor. The post details their current infrastructure and asks for potential gotchas or best practices.
