GitOps deployment workflows with ArgoCD and Flux. Use this skill whenever the user mentions GitOps, ArgoCD, Flux, Flagger, Argo Rollouts, or continuous deployment to Kubernetes. Triggers include setting up ArgoCD or Flux from scratch, designing Git repository structures (monorepo vs polyrepo, app-of-apps), deploying to multiple clusters with ApplicationSets, managing secrets in Git (SOPS, Sealed Secrets, External Secrets Operator), implementing canary or blue-green deployments, troubleshooting sync or reconciliation issues, working with OCI artifacts, and comparing ArgoCD vs Flux.
npx skills add https://github.com/ahmedasmar/devops-claude-skills --skill gitops-workflows
Use this decision tree to determine your starting point:
Do you have GitOps installed?
├─ NO → Need to choose a tool
│ └─ Want UI + easy onboarding? → ArgoCD (Workflow 1)
│ └─ Want modularity + platform engineering? → Flux (Workflow 2)
└─ YES → What's your goal?
├─ Sync issues / troubleshooting → Workflow 7
├─ Multi-cluster deployment → Workflow 4
├─ Secrets management → Workflow 5
├─ Progressive delivery → Workflow 6
├─ Repository structure → Workflow 3
└─ Tool comparison → Read references/argocd_vs_flux.md
Latest Version: v3.1.9 (stable), v3.2.0-rc4 (October 2025)
# Create namespace
kubectl create namespace argocd
# Install ArgoCD 3.x
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/v3.1.9/manifests/install.yaml
# Get admin password
kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 -d
# Port forward to access UI
kubectl port-forward svc/argocd-server -n argocd 8080:443
# Access: https://localhost:8080
→ Template: assets/argocd/install-argocd-3.x.yaml
# CLI method
argocd app create guestbook \
--repo https://github.com/argoproj/argocd-example-apps.git \
--path guestbook \
--dest-server https://kubernetes.default.svc \
--dest-namespace default
# Sync application
argocd app sync guestbook
# List all applications and their sync/health status
argocd app list
# Get detailed status for a specific application
argocd app get <app-name>
# Check applications via kubectl (no ArgoCD CLI needed)
kubectl get applications.argoproj.io -A
Latest Version: v2.7.1 (October 2025)
# Install Flux CLI
brew install fluxcd/tap/flux # macOS
# or: curl -s https://fluxcd.io/install.sh | sudo bash
# Check prerequisites
flux check --pre
# Bootstrap Flux (GitHub)
export GITHUB_TOKEN=<your-token>
flux bootstrap github \
--owner=<org> \
--repository=fleet-infra \
--branch=main \
--path=clusters/production \
--personal
# Enable source-watcher (Flux 2.7+)
flux install --components-extra=source-watcher
→ Template: assets/flux/flux-bootstrap-github.sh
# gitrepository.yaml
apiVersion: source.toolkit.fluxcd.io/v1
kind: GitRepository
metadata:
name: podinfo
namespace: flux-system
spec:
interval: 1m
url: https://github.com/stefanprodan/podinfo
ref:
branch: master
---
# kustomization.yaml
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
name: podinfo
namespace: flux-system
spec:
interval: 5m
path: "./kustomize"
prune: true
sourceRef:
kind: GitRepository
name: podinfo
# Check all Flux resources across namespaces
flux get all -A
# Check Git sources
flux get sources git
# Check kustomization status
flux get kustomizations
Decision: Monorepo or Polyrepo?
Best for: Startups, small teams (< 20 apps), single team
gitops-repo/
├── apps/
│ ├── frontend/
│ ├── backend/
│ └── database/
├── infrastructure/
│ ├── ingress/
│ ├── monitoring/
│ └── secrets/
└── clusters/
├── dev/
├── staging/
└── production/
Best for: Large orgs, multiple teams, clear boundaries
infrastructure-repo/ (Platform team)
app-team-1-repo/ (Team 1)
app-team-2-repo/ (Team 2)
app/
├── base/
│ ├── deployment.yaml
│ ├── service.yaml
│ └── kustomization.yaml
└── overlays/
├── dev/
│ ├── kustomization.yaml
│ └── replica-patch.yaml
├── staging/
└── production/
→ Reference: references/repo_patterns.md | → Script: python3 scripts/validate_gitops_repo.py /path/to/repo
Cluster Generator (deploy to all clusters):
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: cluster-apps
spec:
generators:
- cluster:
selector:
matchLabels:
environment: production
template:
metadata:
name: '{{name}}-myapp'
spec:
source:
repoURL: https://github.com/org/apps
path: myapp
destination:
server: '{{server}}'
→ Template: assets/applicationsets/cluster-generator.yaml
Performance Benefit: 83% faster deployments (30min → 5min)
# Cluster generator
python3 scripts/applicationset_generator.py cluster \
--name my-apps \
--repo-url https://github.com/org/repo \
--output appset.yaml
# Matrix generator (cluster x apps)
python3 scripts/applicationset_generator.py matrix \
--name my-apps \
--cluster-label production \
--directories app1,app2,app3 \
--output appset.yaml
→ Script: scripts/applicationset_generator.py
Hub-and-Spoke: Management cluster manages all clusters
# Bootstrap each cluster
flux bootstrap github --context prod-cluster --path clusters/production
flux bootstrap github --context staging-cluster --path clusters/staging
→ Reference: references/multi_cluster.md
Never commit plain secrets to Git. Choose a solution:
| Solution | Complexity | Best For | 2025 Trend |
|----------|-----------|----------|------------|
| SOPS + age | Medium | Git-centric, flexible | ↗️ Preferred |
| External Secrets Operator | Medium | Cloud-native, dynamic | ↗️ Growing |
| Sealed Secrets | Low | Simple, GitOps-first | → Stable |
Setup:
# Generate age key
age-keygen -o key.txt
# Public key: age1...
# Create .sops.yaml
cat <<EOF > .sops.yaml
creation_rules:
- path_regex: .*.yaml
encrypted_regex: ^(data|stringData)$
age: age1ql3z7hjy54pw3hyww5ayyfg7zqgvc7w3j2elw8zmrj2kg5sfn9aqmcac8p
EOF
# Encrypt secret
kubectl create secret generic my-secret --dry-run=client -o yaml \
--from-literal=password=supersecret > secret.yaml
sops -e secret.yaml > secret.enc.yaml
# Commit encrypted version
git add secret.enc.yaml .sops.yaml
→ Template: assets/secrets/sops-age-config.yaml
Best for: Cloud-native apps, dynamic secrets, automatic rotation
Best for: Simple setup, static secrets, no external dependencies
→ Reference: references/secret_management.md
python3 scripts/secret_audit.py /path/to/repo
→ Script: scripts/secret_audit.py
Canary Deployment:
apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
name: my-app
spec:
strategy:
canary:
steps:
- setWeight: 20
- pause: {duration: 2m}
- setWeight: 50
- pause: {duration: 2m}
- setWeight: 100
→ Template: assets/progressive-delivery/argo-rollouts-canary.yaml
Canary with Metrics Analysis:
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
name: my-app
spec:
analysis:
interval: 1m
threshold: 5
maxWeight: 50
stepWeight: 10
metrics:
- name: request-success-rate
thresholdRange:
min: 99
→ Reference: references/progressive_delivery.md
ArgoCD OutOfSync:
# Check differences
argocd app diff my-app
# Sync application
argocd app sync my-app
# Check health
argocd app list
argocd app get my-app
Flux Not Reconciling:
# Check resources
flux get all
# Check specific kustomization
flux get kustomizations
kubectl describe kustomization my-app -n flux-system
# Force reconcile
flux reconcile kustomization my-app
Detect Drift:
# ArgoCD drift detection
argocd app diff my-app
# Kubernetes manifest drift detection
kubectl diff -f <manifest.yaml>
→ Reference: references/troubleshooting.md
apiVersion: source.toolkit.fluxcd.io/v1beta2
kind: OCIRepository
metadata:
name: podinfo-oci
spec:
interval: 5m
url: oci://ghcr.io/stefanprodan/charts/podinfo
ref:
semver: ">=6.0.0"
verify:
provider: cosign
→ Template: assets/flux/oci-helmrelease.yaml
# Check OCI sources managed by Flux
flux get sources oci
# Get detailed OCI repository status via kubectl
kubectl get ocirepository -A -o json
→ Reference: references/oci_artifacts.md
argocd app list # List applications
argocd app get <app-name> # Get application details
argocd app sync <app-name> # Sync application
argocd app diff <app-name> # View diff
argocd app delete <app-name> # Delete application
flux check # Check Flux status
flux get all # Get all resources
flux reconcile source git <name> # Reconcile immediately
flux reconcile kustomization <name> # Reconcile kustomization
flux suspend kustomization <name> # Suspend
flux resume kustomization <name> # Resume
flux export source git --all > sources.yaml # Export resources
Scripts: applicationset_generator.py | secret_audit.py | validate_gitops_repo.py
References: argocd_vs_flux.md | repo_patterns.md | secret_management.md | progressive_delivery.md | multi_cluster.md | troubleshooting.md | best_practices.md | oci_artifacts.md
Templates: argocd/install-argocd-3.x.yaml | applicationsets/cluster-generator.yaml | flux/flux-bootstrap-github.sh | flux/oci-helmrelease.yaml | secrets/sops-age-config.yaml | progressive-delivery/argo-rollouts-canary.yaml
Assess 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 ahmedasmar/gitops-workflows from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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same name cannot sit side by side — one of them will be ignored.
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