Skill UI

This skill provides a comprehensive guide for safely upgrading the Oracle Cloud Infrastructure (OCI) Python SDK and Terraform provider. It addresses known breaking changes, schema modifications, and version incompatibility issues common in OCI tools. By implementing version pinning, pre-upgrade testing, and structured rollback procedures, users can ensure a stable and reliable migration process for both code and infrastructure definitions.

This advanced skill automates the entire lifecycle of cloud infrastructure deployment. Instead of manual documentation review, it researches current best practices across multi-cloud platforms (AWS, GCP, Azure), synthesizes a coherent deployment strategy, and generates production-grade Infrastructure as Code (IaC). It outputs comprehensive artifacts, including Terraform modules, Kubernetes manifests, CI/CD pipelines, monitoring setups, and cost estimates, ensuring secure and optimized deployment.

This module provides essential architectural abstractions for structuring complex applications using the Webiny framework. It defines core types for various system components, including Admin panels, APIs, Command Line Interfaces (CLI), Infrastructure management, and Project logic. Developers must utilize these imported abstractions to ensure strict type safety and clear separation of concerns in their code.

This guide details the complete architecture and organizational principles of a Webiny project. It covers the core concepts of the flat folder structure, the role of the central `webiny.config.tsx` file, and how to register various types of extensions (API, Admin UI, Infrastructure, and CLI Commands) using declarative JSX components. Use this when developers need to understand where custom code belongs and how to integrate different functional components into the platform.

A comprehensive guide for designing and implementing robust Infrastructure as Code (IaC) using Terraform or OpenTofu. Covers module hierarchy design, best practices for naming conventions, structuring multi-environment pipelines, advanced testing strategies (native tests, Terratest, static analysis), and integrating security scanning (trivy, checkov) into CI/CD workflows.

This workflow systematically analyzes deployed AWS resources and Infrastructure-as-Code (IaC) definitions. It collects usage metrics from CloudWatch and Cost Explorer to identify over-provisioning, unused resources, and inefficient configurations (e.g., right-sizing EC2, S3 lifecycle policies). It generates actionable cost optimization recommendations and creates structured GitHub issues for tracking and implementing savings.

Dep handles the entire journey from local code to production deployment. This skill automates containerization using Docker and docker-compose, establishes robust CI/CD pipelines (linting, testing, security scanning, deployment), manages environment variables and secrets, and generates Infrastructure as Code (Terraform/Pulumi) configurations. It ensures observability and provides rollback procedures.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This tool conducts structured reviews of various Infrastructure-as-Code (IaC) tools (e.g., Terraform, Pulumi) across multiple cloud providers. It generates an actionable report that categorizes findings by severity (Critical, High, Medium, Low), providing detailed remediation steps and a reusable best-practice checklist for continuous security, compliance, and operational auditing.

A comprehensive guide for managing AWS infrastructure as code using the SST v4 (Ion) framework, built on top of Pulumi. This skill covers the entire lifecycle, including writing and editing sst.config.ts, defining high-level components (Function, Bucket, Dynamo), wiring resource links, writing unit tests for infrastructure, and troubleshooting deployments across complex, multi-stack AWS environments.

This skill analyzes existing legacy codebases to detect implicit coding conventions across four meta-architectural dimensions (File Anatomy, State & Control Flow, Infrastructure, Error Handling). It resolves conflicts one by one with the user, generating a structured rules file (.ai-style-rules.md) and optionally installing persistent hooks to prevent AI-generated code style drift in future development.

This skill guides users through Phases 2 (Pseudocode Design) and 3 (Architecture Design) of the SPARC methodology. It translates high-level specifications into detailed technical blueprints, covering algorithm pseudocode, module boundaries, API contracts (using DDD patterns), and infrastructure planning, ensuring a robust plan before writing production code.