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Gitops Workflows

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ahmedasmar
gitops-workflows

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.

Overview

Publisherahmedasmar
Repositorydevops-claude-skills
Skill namegitops-workflows
Stars
203
Forks
38
Bundled files
17
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ahmedasmar on GitHub. Read the source before you install it.

Installation

Install the Gitops Workflows AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ahmedasmar/devops-claude-skills/tree/main/gitops-workflows/skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ahmedasmar/devops-claude-skills.git /tmp/devops-claude-skills
mkdir -p .claude/skills
cp -r /tmp/devops-claude-skills/gitops-workflows/skills .claude/skills/gitops-workflows
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gitops Workflows in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gitops Workflows on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gitops Workflows is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GitOps Workflows

Core Workflow: GitOps Implementation

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

1. Initial Setup: ArgoCD 3.x

Latest Version: v3.1.9 (stable), v3.2.0-rc4 (October 2025)

Quick Install

bash
# 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

ArgoCD 3.x Key Changes

  • Breaking: Annotation-based tracking (default, was labels), RBAC logs enforcement enabled, legacy metrics removed
  • New: Fine-grained RBAC (per-resource permissions), better defaults (resource exclusions), secrets operators endorsement

Deploy Your First Application

bash
# 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

Health Check

bash
# 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

2. Initial Setup: Flux 2.7

Latest Version: v2.7.1 (October 2025)

Quick Install

bash
# 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

Flux 2.7 New Features

  • ✅ Image automation GA
  • ✅ ExternalArtifact and ArtifactGenerator APIs
  • ✅ Source-watcher component for better performance
  • ✅ OpenTelemetry tracing support
  • ✅ CEL expressions for readiness evaluation

Deploy Your First Application

yaml
# 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

Health Check

bash
# Check all Flux resources across namespaces
flux get all -A

# Check Git sources
flux get sources git

# Check kustomization status
flux get kustomizations

3. Repository Structure Design

Decision: Monorepo or Polyrepo?

Monorepo Pattern

Best for: Startups, small teams (< 20 apps), single team

gitops-repo/
├── apps/
│   ├── frontend/
│   ├── backend/
│   └── database/
├── infrastructure/
│   ├── ingress/
│   ├── monitoring/
│   └── secrets/
└── clusters/
    ├── dev/
    ├── staging/
    └── production/

Polyrepo Pattern

Best for: Large orgs, multiple teams, clear boundaries

infrastructure-repo/     (Platform team)
app-team-1-repo/        (Team 1)
app-team-2-repo/        (Team 2)

Environment Structure (Kustomize)

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


4. Multi-Cluster Deployments

ArgoCD ApplicationSets

Cluster Generator (deploy to all clusters):

yaml
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)

Generate ApplicationSets

bash
# 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

Flux Multi-Cluster

Hub-and-Spoke: Management cluster manages all clusters

bash
# 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


5. Secrets Management

Never commit plain secrets to Git. Choose a solution:

Decision Matrix

SolutionComplexityBest For2025 Trend
SOPS + ageMediumGit-centric, flexible↗️ Preferred
External Secrets OperatorMediumCloud-native, dynamic↗️ Growing
Sealed SecretsLowSimple, GitOps-first→ Stable

Option 1: SOPS + age (Recommended 2025)

Setup:

bash
# 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

Option 2: External Secrets Operator (v0.20+)

Best for: Cloud-native apps, dynamic secrets, automatic rotation

Option 3: Sealed Secrets

Best for: Simple setup, static secrets, no external dependencies

→ Reference: references/secret_management.md

Audit Secrets

bash
python3 scripts/secret_audit.py /path/to/repo

→ Script: scripts/secret_audit.py


6. Progressive Delivery

Argo Rollouts (with ArgoCD)

Canary Deployment:

yaml
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

Flagger (with Flux)

Canary with Metrics Analysis:

yaml
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


7. Troubleshooting

Common Issues

ArgoCD OutOfSync:

bash
# 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:

bash
# 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:

bash
# ArgoCD drift detection
argocd app diff my-app

# Kubernetes manifest drift detection
kubectl diff -f <manifest.yaml>

→ Reference: references/troubleshooting.md


8. OCI Artifacts (Flux 2.6+, GA June 2025)

Use OCIRepository for Helm Charts

yaml
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

Verify OCI Artifacts

bash
# 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

Quick Reference Commands

ArgoCD

bash
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

bash
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

Resources Summary

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gitops Workflows AI skill do?

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.

Why use Gitops Workflows on TypingMind?

Because you install it once and use it with any model. Gitops Workflows is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gitops Workflows in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ahmedasmar/devops-claude-skills/tree/main/gitops-workflows/skills. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gitops Workflows?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gitops Workflows?

As many as you like. As long as a model supports skills, you can use Gitops Workflows with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gitops Workflows AI skill free?

It is published on GitHub by ahmedasmar. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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