Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure management.
npx skills add https://github.com/wshobson/agents --skill gitops-workflow
Complete guide to implementing GitOps workflows with ArgoCD and Flux for automated Kubernetes deployments.
Implement declarative, Git-based continuous delivery for Kubernetes using ArgoCD or Flux CD, following OpenGitOps principles.
# Create namespace
kubectl create namespace argocd
# Install ArgoCD
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml
# Get admin password
kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 -d
Reference: See references/argocd-setup.md for detailed setup
gitops-repo/
├── apps/
│ ├── production/
│ │ ├── app1/
│ │ │ ├── kustomization.yaml
│ │ │ └── deployment.yaml
│ │ └── app2/
│ └── staging/
├── infrastructure/
│ ├── ingress-nginx/
│ ├── cert-manager/
│ └── monitoring/
└── argocd/
├── applications/
└── projects/
# argocd/applications/my-app.yaml
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: my-app
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/org/gitops-repo
targetRevision: main
path: apps/production/my-app
destination:
server: https://kubernetes.default.svc
namespace: production
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- CreateNamespace=true
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: applications
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/org/gitops-repo
targetRevision: main
path: argocd/applications
destination:
server: https://kubernetes.default.svc
namespace: argocd
syncPolicy:
automated: {}
# Install Flux CLI
curl -s https://fluxcd.io/install.sh | sudo bash
# Bootstrap Flux
flux bootstrap github \
--owner=org \
--repository=gitops-repo \
--branch=main \
--path=clusters/production \
--personal
apiVersion: source.toolkit.fluxcd.io/v1
kind: GitRepository
metadata:
name: my-app
namespace: flux-system
spec:
interval: 1m
url: https://github.com/org/my-app
ref:
branch: main
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
name: my-app
namespace: flux-system
spec:
interval: 5m
path: ./deploy
prune: true
sourceRef:
kind: GitRepository
name: my-app
ArgoCD:
syncPolicy:
automated:
prune: true # Delete resources not in Git
selfHeal: true # Reconcile manual changes
allowEmpty: false
retry:
limit: 5
backoff:
duration: 5s
factor: 2
maxDuration: 3m
Flux:
spec:
interval: 1m
prune: true
wait: true
timeout: 5m
Reference: See references/sync-policies.md
apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
name: my-app
spec:
replicas: 5
strategy:
canary:
steps:
- setWeight: 20
- pause: { duration: 1m }
- setWeight: 50
- pause: { duration: 2m }
- setWeight: 100
strategy:
blueGreen:
activeService: my-app
previewService: my-app-preview
autoPromotionEnabled: false
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
name: db-credentials
spec:
refreshInterval: 1h
secretStoreRef:
name: aws-secrets-manager
kind: SecretStore
target:
name: db-credentials
data:
- secretKey: password
remoteRef:
key: prod/db/password
# Encrypt secret
kubeseal --format yaml < secret.yaml > sealed-secret.yaml
# Commit sealed-secret.yaml to Git
10. Test changes in staging first
Sync failures:
argocd app get my-app
argocd app sync my-app --prune
Out of sync status:
argocd app diff my-app
argocd app sync my-app --force
k8s-manifest-generator - For creating manifestshelm-chart-scaffolding - For packaging applicationsAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take wshobson/gitops-workflow from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.