DNYoussef /============================================================================/ /* 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
# 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
# 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
helm create my-app
cd my-app
Step 2: Define Values.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
# 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]