Implement GitOps with ArgoCD for declarative Kubernetes deployments. Configure applications, manage sync policies, implement progressive delivery, and automate deployments from Git repositories. Use when implementing GitOps workflows or continuous deployment to Kubernetes.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill argocd-gitops
Implement declarative continuous delivery for Kubernetes with ArgoCD.
Use this skill when:
# 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
# Port forward to access UI
kubectl port-forward svc/argocd-server -n argocd 8080:443
# Login with CLI
argocd login localhost:8080
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/org/myapp-manifests.git
targetRevision: main
path: environments/production
destination:
server: https://kubernetes.default.svc
namespace: myapp
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- CreateNamespace=true
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp-helm
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/org/myapp-chart.git
targetRevision: main
path: charts/myapp
helm:
valueFiles:
- values.yaml
- values-production.yaml
parameters:
- name: replicaCount
value: "3"
- name: image.tag
value: "2.0.0"
destination:
server: https://kubernetes.default.svc
namespace: myapp
syncPolicy:
automated:
prune: true
selfHeal: true
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp-kustomize
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/org/myapp-manifests.git
targetRevision: main
path: overlays/production
kustomize:
images:
- myapp=myregistry/myapp:2.0.0
destination:
server: https://kubernetes.default.svc
namespace: myapp
apiVersion: argoproj.io/v1alpha1
kind: AppProject
metadata:
name: myproject
namespace: argocd
spec:
description: My Project
sourceRepos:
- https://github.com/org/*
destinations:
- namespace: myapp-*
server: https://kubernetes.default.svc
clusterResourceWhitelist:
- group: ''
kind: Namespace
namespaceResourceWhitelist:
- group: '*'
kind: '*'
roles:
- name: developer
description: Developer role
policies:
- p, proj:myproject:developer, applications, get, myproject/*, allow
- p, proj:myproject:developer, applications, sync, myproject/*, allow
groups:
- developers
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: myapp-environments
namespace: argocd
spec:
generators:
- git:
repoURL: https://github.com/org/myapp-manifests.git
revision: main
directories:
- path: environments/*
template:
metadata:
name: 'myapp-{{path.basename}}'
spec:
project: default
source:
repoURL: https://github.com/org/myapp-manifests.git
targetRevision: main
path: '{{path}}'
destination:
server: https://kubernetes.default.svc
namespace: 'myapp-{{path.basename}}'
syncPolicy:
automated:
prune: true
selfHeal: true
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: myapp-clusters
namespace: argocd
spec:
generators:
- list:
elements:
- cluster: production
url: https://prod-cluster.example.com
- cluster: staging
url: https://staging-cluster.example.com
template:
metadata:
name: 'myapp-{{cluster}}'
spec:
project: default
source:
repoURL: https://github.com/org/myapp-manifests.git
targetRevision: main
path: 'environments/{{cluster}}'
destination:
server: '{{url}}'
namespace: myapp
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: myapp-matrix
namespace: argocd
spec:
generators:
- matrix:
generators:
- git:
repoURL: https://github.com/org/myapp-manifests.git
revision: main
directories:
- path: apps/*
- list:
elements:
- env: staging
- env: production
template:
metadata:
name: '{{path.basename}}-{{env}}'
spec:
project: default
source:
repoURL: https://github.com/org/myapp-manifests.git
targetRevision: main
path: '{{path}}/overlays/{{env}}'
destination:
server: https://kubernetes.default.svc
namespace: '{{path.basename}}-{{env}}'
syncPolicy:
automated:
prune: true # Delete resources not in Git
selfHeal: true # Revert manual changes
allowEmpty: false # Don't sync empty directories
syncOptions:
- CreateNamespace=true
- PrunePropagationPolicy=foreground
- PruneLast=true
retry:
limit: 5
backoff:
duration: 5s
factor: 2
maxDuration: 3m
# In Kubernetes manifests
apiVersion: v1
kind: ConfigMap
metadata:
name: myconfig
annotations:
argocd.argoproj.io/sync-wave: "-1" # Sync first
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
annotations:
argocd.argoproj.io/sync-wave: "0" # Sync second
apiVersion: batch/v1
kind: Job
metadata:
name: migration
annotations:
argocd.argoproj.io/hook: PreSync
argocd.argoproj.io/hook-delete-policy: HookSucceeded
spec:
template:
spec:
containers:
- name: migrate
image: myapp:latest
command: ["./migrate.sh"]
restartPolicy: Never
# List applications
argocd app list
# Get application details
argocd app get myapp
# Sync application
argocd app sync myapp
# Force sync (ignore differences)
argocd app sync myapp --force
# View diff
argocd app diff myapp
# Rollback
argocd app rollback myapp
# Delete application
argocd app delete myapp
# View logs
argocd app logs myapp
# Hard refresh (clear cache)
argocd app get myapp --hard-refresh
apiVersion: v1
kind: Secret
metadata:
name: private-repo
namespace: argocd
labels:
argocd.argoproj.io/secret-type: repository
stringData:
url: https://github.com/org/private-repo.git
username: git
password: ghp_xxxx
---
# SSH key
apiVersion: v1
kind: Secret
metadata:
name: private-repo-ssh
namespace: argocd
labels:
argocd.argoproj.io/secret-type: repository
stringData:
url: [email protected]:org/private-repo.git
sshPrivateKey: |
-----BEGIN OPENSSH PRIVATE KEY-----
...
-----END OPENSSH PRIVATE KEY-----
apiVersion: v1
kind: ConfigMap
metadata:
name: argocd-notifications-cm
namespace: argocd
data:
service.slack: |
token: $slack-token
template.app-deployed: |
message: Application {{.app.metadata.name}} is now {{.app.status.sync.status}}.
trigger.on-deployed: |
- when: app.status.operationState.phase in ['Succeeded']
send: [app-deployed]
Problem: Resources show differences but are correct
Solution: Configure ignore differences
spec:
ignoreDifferences:
- group: apps
kind: Deployment
jsonPointers:
- /spec/replicas
Problem: ArgoCD cannot clone repository
Solution: Check repository secret, verify URL and credentials
Problem: Application never becomes synced
Solution: Check resource status, review events, verify manifests
Problem: Application shows degraded health
Solution: Check custom health checks, verify probe configurations
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 bagelhole/argocd-gitops 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.