k8s-ai

🧠 Tenant repository bootstrapped by k8s-infrastructure that contains the manifests for AI related applications

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Install / Use

claude mcp add mmontes11 -- npx -y github:mmontes11/k8s-ai

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill

🔌

MCP Server

Model Context Protocol server

Quality Score

72/100

Our assessment of k8s-ai

k8s-ai scores 72/100 on our quality scale, 885th of 968 AI & Machine Learning skills we index.

Its MCP Server is 4.1 KB long, well organised into 17 sections with 1 code example: a solid amount of guidance for an agent.

It has 3 GitHub stars, so there is little community track record yet; judge it on its content.

Substance

26/30

Structure

17/20

Description

12/15

Adoption

3/20

Freshness

15/15

Maintenance, license and trust

The repository was last updated today, so k8s-ai is actively maintained.
It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-10-09. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

k8s-ai compared with similar skills

All 4 of these similar skills score higher than k8s-ai; compare them before choosing.

Skill	Score	Stars	Updated	Format
k8s-ai (this skill)by mmontes11	72	3	today	MCP Server
claude-memby thedotmack	100	98.6k	today	CLAUDE.md
Agent-Reachby Panniantong	100	94.3k	1d ago	CLAUDE.md
Understand-Anythingby Egonex-AI	100	85.7k	3d ago	CLAUDE.md
headroomby headroomlabs-ai	100	74.8k	today	CLAUDE.md

Frequently asked questions

How do I install k8s-ai?: Run claude mcp add mmontes11 -- npx -y github:mmontes11/k8s-ai. The install tabs above show the steps for each supported agent.
Which AI agents does k8s-ai work with?: It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is k8s-ai safe to use?: Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is Apache-2.0-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is k8s-ai still maintained?: The repository was last updated today, so k8s-ai is actively maintained.

Skill content

View source on GitHub

🧠 k8s-ai

Tenant repository bootstrapped by k8s-infrastructure that contains the manifests for AI related applications

Overview

This repository manages AI workloads on Kubernetes using GitOps with Flux CD. It includes deployments for LLM inference services, web UIs, and model serving infrastructure.

Applications

Open WebUI

Path: ./apps/open-webui
Type: HelmRelease (ollama-webui chart)
Description: Web interface for interacting with LLMs
Features:
- Persistent storage via PVC
- Integration with Ollama backend
- Model access control bypass enabled

ComfyUI

Path: ./apps/comfyui
Type: Native Kubernetes resources
Description: Graph-based interface for Stable Diffusion
Image: mmontes11/docker-comfyui
Features:
- Persistent volume for model caching
- Replication source/destination for data synchronization
- RESTic backup support

n8n

Path: ./apps/n8n
Type: HelmRelease (n8n helm chart)
Description: Workflow automation and integration platform
Features:
- Persistent storage via PVC
- RESTic backup support
- Replication source/destination for data synchronization

opencode

Path: ./apps/opencode
Type: Native Kubernetes resources
Description: Coding agent and AI workspace for interactive development
Image: mmontes11/docker-opencode
Features:
- NVIDIA GPU support for accelerated model training and inference
- Persistent storage (100Gi PVC)
- Pre-configured development environment with tools
- RESTic backup support
- Replication source/destination for data synchronization
- Integration with GitHub, HuggingFace, and n8n via tokens

Infrastructure

Model Serving

Ollama

Path: ./infrastructure/ollama
Description: Lightweight LLM inference server
Features:
- Native GPU support
- Simple HTTP API
- Model caching

llama.cpp

Path: ./infrastructure/llamacpp
Description: High-performance C/C++ inference engine optimized for CPU and GPU
Features:
- Qwen3.6 MTP model support with 1.4-2.2x faster inference
- 256k context window for agentic AI workflows
- StatefulSet deployment with persistent storage
- Prometheus ServiceMonitor integration
- Ingress routing via HTTPRoute

vLLM

Path: ./infrastructure/vllm
Description: High-throughput LLM serving with PagedAttention
Use Case: Production workloads requiring high concurrency

KServe

Path: ./infrastructure/kserve
Example: ./examples/llminferenceservice.yaml
Description: Kubernetes-native ML serving platform
Features:
- LLMInferenceService CRD
- Custom model templates

MCP Servers

MCP Kubernetes: Kubernetes model context protocol server
MCP Grafana: Grafana monitoring integration
MCP GitHub: GitHub API integration
MCP Photoprism: Photo management (mmontes & xiaowen)

Architecture

├── apps/                    # Application deployments
│   ├── comfyui/            # ComfyUI deployment
│   ├── n8n/                # n8n workflow automation
│   ├── opencode/           # opencode AI development workspace
│   └── open-webui/         # Open WebUI deployment
├── clusters/               # Cluster-specific configurations
│   └── homelab/
│       ├── apps.yaml       # Application Kustomizations
│       ├── infrastructure.yaml
│       └── namespaces.yaml
├── examples/               # Example configurations
│   └── llminferenceservice.yaml
└── infrastructure/         # Shared infrastructure
    ├── kserve/             # KServe ML serving
    ├── vllm/               # vLLM serving engine
    ├── llamacpp/           # llama.cpp inference engine
    ├── lws/                # LeaderWorkerSet
    ├── ollama/             # Ollama LLM backend
    └── mcp-*/             # MCP server integrations

AI Benchmarks

LLM benchmarks using llama.cpp on Kubernetes: mmontes11/llm-bench

License

MIT

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mmontes11

View profile

View on GitHub

GitHub Stars3

CategoryAI

Updated8h ago

Forks0

mmontes11/k8s-ai

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 low