Agent skill

kubernetes-specialist

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/operations/cloud-platforms/kubernetes-specialist

SKILL.md

/============================================================================/ /* KUBERNETES-SPECIALIST SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: kubernetes-specialist version: 1.0.0 description: | [assert|neutral] Kubernetes orchestration expert for Helm chart development, custom operators and CRDs, service mesh (Istio/Linkerd), auto-scaling strategies (HPA/VPA/Cluster Autoscaler), multi-cluster management, and [ground:given] [conf:0.95] [state:confirmed] category: Cloud Platforms tags:

  • general author: system cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute kubernetes-specialist workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic Cloud Platforms processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "kubernetes-specialist", category: "Cloud Platforms", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["kubernetes-specialist", "Cloud Platforms", "workflow"], context: "user needs kubernetes-specialist capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

Kubernetes Specialist

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Expert Kubernetes orchestration for cloud-native applications with production-grade deployments.

Purpose

Comprehensive Kubernetes expertise including Helm charts, custom operators, service mesh, auto-scaling, and GitOps. Ensures K8s deployments are resilient, secure, observable, and cost-effective.

When to Use

  • Deploying microservices to Kubernetes
  • Creating Helm charts for reusable deployments
  • Implementing auto-scaling (HPA, VPA, Cluster Autoscaler)
  • Setting up service mesh for advanced networking
  • Building custom operators with Operator SDK
  • Implementing GitOps with ArgoCD or Flux
  • Optimizing pod scheduling and resource allocation

Prerequisites

Required: Docker, kubectl, basic K8s concepts (Pods, Services, Deployments)

Agents: system-architect, cicd-engineer, perf-analyzer, security-manager

Core Workflows

Workflow 1: Production-Grade Deployment

Step 1: Create Deployment Manifest

yaml
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
        version: v1
    spec:
      containers:
      - name: app
        image: myregistry/my-app:v1.0.0
        ports:
        - containerPort: 8080
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
          limits:
            memory: "256Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
        securityContext:
          runAsNonRoot: true
          readOnlyRootFilesystem: true
          allowPrivilegeEscalation: false
          capabilities:
            drop:
            - ALL
      affinity:
        podAntiAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100
            podAffinityTerm:
              labelSelector:
                matchExpressions:
                - key: app
                  operator: In
                  values:
                  - my-app
              topologyKey: kubernetes.io/hostname

Step 2: Create Service and Ingress

yaml
# service.yaml
apiVersion: v1
kind: Service
metadata:
  name: my-app
spec:
  selector:
    app: my-app
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: ClusterIP

---
# ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-app
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/rate-limit: "100"
spec:
  ingressClassName: nginx
  tls:
  - hosts:
    - my-app.example.com
    secretName: my-app-tls
  rules:
  - host: my-app.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: my-app
            port:
              number: 80

Workflow 2: Helm Chart Development

Step 1: Create Helm Chart

bash
helm create my-app
cd my-app

Step 2: Define Values.yaml

yaml
# values.yaml
replicaCount: 3

image:
  repository: myregistry/my-app
  tag: "v1.0.0"
  pullPolicy: IfNotPresent

resources:
  requests:
    memory: "128Mi"
    cpu: "100m"
  limits:
    memory: "256Mi"
    cpu: "500m"

autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 70

ingress:
  enabled: true
  className: nginx
  hosts:
    - host: my-app.example.com
      paths:
        - path: /
          pathType: Prefix
  tls:
    - secretName: my-app-tls
      hosts:
        - my-app.example.com

Step 3: Template Deployment

yaml
# templates/depl

/*----------------------------------------------------------------------------*/
/* S4 SUCCESS CRITERIA                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := {
  primary: "Skill execution completes successfully",
  quality: "Output meets quality thresholds",
  verification: "Results validated against requirements"
} [ground:given] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S5 MCP INTEGRATION                                                          */
/*----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := {
  memory_mcp: "Store execution results and patterns",
  tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"]
} [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S6 MEMORY NAMESPACE                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := {
  pattern: "skills/Cloud Platforms/kubernetes-specialist/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "kubernetes-specialist-{session_id}",
  WHEN: "ISO8601_timestamp",
  PROJECT: "{project_name}",
  WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S7 SKILL COMPLETION VERIFICATION                                            */
/*----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := {
  agent_spawning: "Spawn agents via Task()",
  registry_validation: "Use registry agents only",
  todowrite_called: "Track progress with TodoWrite",
  work_delegation: "Delegate to specialized agents"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S8 ABSOLUTE RULES                                                           */
/*----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* PROMISE                                                                     */
/*----------------------------------------------------------------------------*/

[commit|confident] <promise>KUBERNETES_SPECIALIST_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]

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