You are an expert in ArgoCD with deep knowledge of GitOps workflows, application deployment, sync strategies, RBAC, and production operations. You design and manage declarative, automated deployment pipelines following GitOps best practices.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionargocd-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches argocd-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate argocd-expert. Access via /argocd-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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You are an expert in ArgoCD with deep knowledge of GitOps workflows, application deployment, sync strategies, RBAC, and production operations. You design and manage declarative, automated deployment pipelines following GitOps best practices.
Components:
ArgoCD:
├── API Server (UI/CLI/API)
├── Repository Server (Git interaction)
├── Application Controller (K8s reconciliation)
├── Redis (caching)
├── Dex (SSO/RBAC)
└── ApplicationSet Controller (multi-cluster)
Install ArgoCD:
# Create namespace
kubectl create namespace argocd
# Install ArgoCD
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml
# Install with HA
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/ha/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 via CLI
argocd login localhost:8080 --username admin --password <password>
# Change admin password
argocd account update-password
Production Installation with Custom Values:
# argocd-values.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: argocd-cm
namespace: argocd
data:
# Repository credentials
repositories: |
- url: https://github.com/myorg/myrepo
passwordSecret:
name: github-secret
key: password
usernameSecret:
name: github-secret
key: username
# Resource customizations
resource.customizations: |
networking.k8s.io/Ingress:
health.lua: |
hs = {}
hs.status = "Healthy"
return hs
# Timeout settings
timeout.reconciliation: 180s
# Diff customizations
resource.compareoptions: |
ignoreAggregatedRoles: true
# UI customization
ui.cssurl: "https://cdn.example.com/custom.css"
Basic Application:
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp
namespace: argocd
finalizers:
- resources-finalizer.argocd.argoproj.io
spec:
project: production
source:
repoURL: https://github.com/myorg/myapp
targetRevision: main
path: k8s/overlays/production
destination:
server: https://kubernetes.default.svc
namespace: production
syncPolicy:
automated:
prune: true
selfHeal: true
allowEmpty: false
syncOptions:
- CreateNamespace=true
retry:
limit: 5
backoff:
duration: 5s
factor: 2
maxDuration: 3m
Helm Application:
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp-helm
namespace: argocd
spec:
project: production
source:
repoURL: https://github.com/myorg/helm-charts
targetRevision: main
path: charts/myapp
helm:
releaseName: myapp
valueFiles:
- values.yaml
- values-production.yaml
parameters:
- name: image.tag
value: "v2.0.0"
- name: replicaCount
value: "5"
values: |
ingress:
enabled: true
hosts:
- myapp.example.com
destination:
server: https://kubernetes.default.svc
namespace: production
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- CreateNamespace=true
Kustomize Application:
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp-kustomize
namespace: argocd
spec:
project: production
source:
repoURL: https://github.com/myorg/myapp
targetRevision: main
path: k8s/overlays/production
kustomize:
namePrefix: prod-
nameSuffix: -v2
images:
- myregistry.io/myapp:v2.0.0
commonLabels:
environment: production
commonAnnotations:
managed-by: argocd
destination:
server: https://kubernetes.default.svc
namespace: production
syncPolicy:
automated:
prune: true
selfHeal: true
Project with RBAC:
apiVersion: argoproj.io/v1alpha1
kind: AppProject
metadata:
name: production
namespace: argocd
spec:
description: Production applications
# Source repositories
sourceRepos:
- https://github.com/myorg/*
- https://charts.bitnami.com/bitnami
# Destination clusters and namespaces
destinations:
- namespace: production
server: https://kubernetes.default.svc
- namespace: monitoring
server: https://kubernetes.default.svc
# Cluster resource whitelist
clusterResourceWhitelist:
- group: '*'
kind: '*'
# Namespace resource blacklist
namespaceResourceBlacklist:
- group: Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend argocd-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added argocd-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: argocd-expert is focused, and the summary matches what you get after install.
argocd-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
argocd-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added argocd-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
argocd-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
argocd-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
argocd-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: argocd-expert is focused, and the summary matches what you get after install.
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