Agent skill

Gitops Argocd

ArgoCD implements GitOps for Kubernetes by continuously reconciling cluster state to desired state defined in Git. This skill covers GitOps principles, ArgoCD architecture, installation and setup, App

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SKILL.md

Gitops Argocd

Skill Profile

(Select at least one profile to enable specific modules)

  • DevOps
  • Backend
  • Frontend
  • AI-RAG
  • Security Critical

Overview

ArgoCD implements GitOps for Kubernetes by continuously reconciling cluster state to desired state defined in Git. This skill covers GitOps principles, ArgoCD architecture, installation and setup, Application CRDs, sync strategies, Application Sets, sync waves and hooks, health checks, resource hooks, secrets management, multi-tenancy and RBAC, SSO integration, notifications, ArgoCD vs Flux, CI/CD integration, rollback strategies, monitoring, and disaster recovery.

Why This Matters

  • Git as Source of Truth: All infrastructure and application state defined in Git
  • Automated Reconciliation: ArgoCD continuously syncs cluster to desired state
  • Visibility: Full audit trail of all changes through Git history
  • Progressive Delivery: Sync waves enable controlled, staged deployments

Core Concepts & Rules

1. Core Principles

  • Follow established patterns and conventions
  • Maintain consistency across codebase
  • Document decisions and trade-offs

2. Implementation Guidelines

  • Start with the simplest viable solution
  • Iterate based on feedback and requirements
  • Test thoroughly before deployment

Inputs / Outputs / Contracts

  • Inputs:

    • Kubernetes cluster with ArgoCD installed
    • Git repository with application manifests
    • Application CRD definitions
    • Docker images for deployment
    • Configuration files (values, overlays)
  • Entry Conditions:

    • Kubernetes cluster is running and accessible
    • Git repository is configured and accessible
    • ArgoCD is installed and configured
    • Container registry is available
  • Outputs:

    • Configured ArgoCD Application resources
    • Deployed applications to Kubernetes
    • Sync status and health checks
    • GitOps workflows with proper version control
    • Secrets management integration
  • Artifacts Required (Deliverables):

    • Application CRD YAML files
    • ApplicationSet CRD YAML files
    • Helm chart values files
    • Kustomization overlays
    • ArgoCD configuration manifests
    • Documentation for GitOps workflows
  • Acceptance Evidence:

    • Screenshot of ArgoCD UI showing applications
    • Screenshot of sync status showing successful deployment
    • Screenshot of health check status
    • Screenshot of Git commit history
  • Success Criteria:

    • Applications sync successfully to cluster
    • Health checks pass for all services
    • Sync status shows "Synced" or "Operation Succeeded"
    • Git commits are created for all changes

Skill Composition

  • Depends on: kubernetes-deployment, helm-charts, terraform-infrastructure
  • Compatible with: ci-cd-github-actions, service-orchestration
  • Conflicts with: None - ArgoCD can coexist with other GitOps tools
  • Related Skills: kubernetes-platform, deployment-patterns

Quick Start / Implementation Example

  1. Review requirements and constraints
  2. Set up development environment
  3. Implement core functionality following patterns
  4. Write tests for critical paths
  5. Run tests and fix issues
  6. Document any deviations or decisions
python
# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

  • Assumptions:
    • Development environment is properly configured
    • Required dependencies are available
    • Team has basic understanding of domain
  • Constraints:
    • Must follow existing codebase conventions
    • Time and resource limitations
    • Compatibility requirements
  • Non-goals:
    • This skill does not cover edge cases outside scope
    • Not a replacement for formal training

Compatibility & Prerequisites

  • Supported Versions:
    • Python 3.8+
    • Node.js 16+
    • Modern browsers (Chrome, Firefox, Safari, Edge)
  • Required AI Tools:
    • Code editor (VS Code recommended)
    • Testing framework appropriate for language
    • Version control (Git)
  • Dependencies:
    • Language-specific package manager
    • Build tools
    • Testing libraries
  • Environment Setup:
    • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Type Focus Area Required Scenarios / Mocks
Unit Core Logic Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
Integration DB / API All external API calls or database connections must be mocked during unit tests
E2E User Journey Critical user flows to test
Performance Latency / Load Benchmark requirements
Security Vuln / Auth SAST/DAST or dependency audit
Frontend UX / A11y Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

  • Top Threats: Injection attacks, authentication bypass, data exposure
  • Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
  • Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
  • Authorization: Validate user permissions before state changes

2. Performance & Resources

  • Execution Efficiency: Consider time complexity for algorithms
  • Memory Management: Use streams/pagination for large data
  • Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

  • Design Pattern: Follow SOLID principles, use Dependency Injection
  • Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

  • Logging Standards: Structured JSON, include trace IDs request_id
  • Metrics: Track error_rate, latency, queue_depth
  • Error Handling: Standardized error codes, no bare except
  • Observability Artifacts:
    • Log Fields: timestamp, level, message, request_id
    • Metrics: request_count, error_count, response_time
    • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

  • Thinking Process: Analyze root cause before fixing. Do not brute-force.
  • Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
  • Self-Review: Check against Guardrails & Anti-patterns before finalizing.
  • Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

  • Tests passed + coverage met
  • Lint/Typecheck passed
  • Logging/Metrics/Trace implemented
  • Security checks passed
  • Documentation/Changelog updated
  • Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

  • Don't: Log PII, catch-all exception, N+1 queries
  • ⚠️ Watch out for: Common symptoms and quick fixes
  • 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

  • Internal documentation and examples
  • Official documentation and best practices
  • Community resources and discussions

Versioning & Changelog

  • Version: 1.0.0
  • Changelog:
    • 2026-02-22: Initial version with complete template structure

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