ops-devops-platform
DevOps and platform engineering patterns: Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, and cloud-native ops.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
159 skills found
DevOps and platform engineering patterns: Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, and cloud-native ops.
Create structured specifications for platform changes including GitHub issues, SDD templates, and automated type inference for infrastructure and security.
A framework for building modular AI agent rigs using Nix, featuring parametrable skills, knowledge management, and automated tool configuration.
CMMI-based SDLC router providing process guidance, requirements management, architectural decision support, quality assurance, and governance for GitHub and Azure DevOps workflows.
Universal CLI tool to convert and synchronize AI agent skills between Claude Code and Gemini CLI extensions.
Full-stack SDLC agent workflow managing the entire production lifecycle from intake and planning to automated testing, CI/CD, and infrastructure deployment using MCP tools.
Optimize developer experience for multi-component solutions: standardize onboarding, inner-loop, debugging, and cross-platform setup to eliminate friction and tribal knowledge.
AI-driven GitHub project management using swarm coordination, automated issue triage, project board synchronization, and intelligent task decomposition for efficient development workflows.
Manage test infrastructure with IaC, Docker, and service virtualization. Optimize testing costs, ensure dev/prod environment parity, and automate environment provisioning for consistent, scalable software testing.
Implement the 'Engineering as Marketing' growth strategy: build free SEO-driven utility tools to drive organic traffic, capture leads, and convert visitors into customers without ad spend.
Orchestrates multi-agent iterative refinement for high-quality OpenClaw skill development, ensuring rigorous testing and lifecycle management.
A framework for creating, testing, and managing autonomous AI subagents within project environments using Test-Driven Development principles.