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name	k8s-pod-health-investigator
description	Investigate unhealthy pods in a Kubernetes namespace. Use when pods are in CrashLoopBackOff, ImagePullBackOff, OOMKilled, or showing high restart counts. Covers pod status, log analysis, deployment history, event correlation, and self-healing recommendations.
version	1.0.0
compatibility	kubectl, HERMES_LAB_MODE=mock|live
metadata	hermes category tags devops kubernetes pods health crashloopbackoff oomkilled diagnosis sre

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Step 1.2

This alarm indicates acute resource pressure on your catalog-api instance. Here's the immediate
  triage:

  Critical Status

  - Instance: i-0abc123def456001 in us-east-1

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In the context of the Common Architecture Language Model (CALM), patterns are described as the project's "real superpower." They serve as a mechanism for architects to convey specific architecture opinions to developers, who then use these patterns to create architectures for their specific business problems.

Core Functions and Structure

• Defining Expectations: Patterns allow architects to specify exactly what is required for a deployment. For example, an MCP (Model Context Protocol) server deployment pattern might include interfaces for container image names and ports.
• Constraints and Consistency: By defining specific requirements within a pattern, architects ensure that downstream implementations remain consistent. This allows for the creation of a "secure pattern" that can be used to deploy various services (like different APIs) while maintaining the same security standards.
• Placeholders: When a developer generates an architecture from a pattern, it often inc

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loki:
  commonConfig:
    replication_factor: 1
  schemaConfig:
    configs:
      - from: "2024-04-01"
        store: tsdb
        object_store: s3
        schema: v13
        index:

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FROM openeuler/vllm-cpu:0.9.1-oe2403lts

# Patch the cpu_worker.py to handle zero NUMA nodes
RUN sed -i 's/cpu_count_per_numa = cpu_count \/\/ numa_size/cpu_count_per_numa = cpu_count \/\/ numa_size if numa_size > 0 else cpu_count/g' \
    /workspace/vllm/vllm/worker/cpu_worker.py

ENV VLLM_TARGET_DEVICE=cpu \
    VLLM_CPU_KVCACHE_SPACE=1 \
    OMP_NUM_THREADS=2 \
    OPENBLAS_NUM_THREADS=1 \

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This Dockerfile builds a container for running vLLM (Large Language Model inference engine) on CPU with specific patches and optimizations. Here's a breakdown:

Base Image

FROM openeuler/vllm-cpu:0.9.1-oe2403lts

• Uses OpenEuler Linux distribution's pre-built vLLM image (version 0.9.1)
• Built for CPU inference (not GPU)
• Based on OpenEuler 24.03 LTS

Critical Patch (Lines 4-5)

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Lab: Using kubectl-ai --mcp-server with Cursor to Inspect the atharva-ml Namespace

0. Lab Goals

By the end of this lab you’ll be able to:

• Run kubectl-ai as an MCP server.
• Wire it into Cursor via mcp.json.
• Use Cursor chat + kubectl-ai tools to:

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0) Repo layout (GitOps view)

add the existing code

git status

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FROM openeuler/vllm-cpu:0.9.1-oe2403lts

# Patch the cpu_worker.py to handle zero NUMA nodes
RUN sed -i 's/cpu_count_per_numa = cpu_count \/\/ numa_size/cpu_count_per_numa = cpu_count \/\/ numa_size if numa_size > 0 else cpu_count/g' \
    /workspace/vllm/vllm/worker/cpu_worker.py

ENV VLLM_TARGET_DEVICE=cpu \
    VLLM_CPU_KVCACHE_SPACE=1 \
    OMP_NUM_THREADS=2 \
    OPENBLAS_NUM_THREADS=1 \

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deploymentMode: SingleBinary

singleBinary:
  replicas: 1

loki:
  commonConfig:
    replication_factor: 1

  # Required for new installs