Skill UI

Terraform module creation for AWS — reusable modules, state management, and HCL best practices. Use when building or reviewing Terraform AWS infrastructure.

A senior specialist skilled in implementing Infrastructure as Code (IaC) using Terraform across major cloud platforms like AWS, Azure, and GCP. Expertise includes designing modular architectures, managing remote state (locking, encryption), enforcing security policies, and guiding the full deployment lifecycle from validation to secure application. Ideal for DevOps and Cloud Engineering roles.

A comprehensive, multi-phase workflow designed for Infrastructure as Code (IaC) using Terraform. It guides users through provisioning cloud resources, creating reusable modules, managing complex state, handling multi-environment deployments, and integrating robust CI/CD pipelines and security practices. Ideal for establishing scalable, repeatable, and auditable cloud infrastructure.

A comprehensive collection of production-ready, reusable Terraform modules designed for standardized infrastructure provisioning across major cloud platforms: AWS, Azure, and GCP. This library enforces Infrastructure as Code (IaC) best practices, allowing users to build, test, and deploy multi-cloud compatible components efficiently and reliably.

Provides Terraform and OpenTofu best-practice guidance for module design, testing selection, CI/CD pipeline setup, security scanning, and architecture decisions in production infrastructure-as-code projects.

Specializes in Terraform/OpenTofu automation, covering advanced module architecture, secure state management, multi-environment strategies, and CI/CD policy-driven workflows for enterprise-grade infrastructure.

Provides automated guidance for Terraform state manager tasks in advanced DevOps workflows, covering Kubernetes, CI/CD, monitoring, and infrastructure-as-code best practices.

Utilizes structured critical reasoning to rigorously stress-test ideas, plans, and decisions. This skill allows you to play devil's advocate, conduct pre-mortems, execute red-teaming exercises, or audit underlying assumptions. It employs multiple advanced modes, including Socratic questioning, Hegelian dialectics, and Falsificationism, to identify blind spots, weaknesses, and unstated assumptions before commitment.

This skill maps identified security threats to appropriate controls and mitigation strategies. It is essential for prioritizing security investments, creating structured remediation roadmaps, validating existing control coverage, and designing robust defense-in-depth architectures. Use it during risk treatment planning and security architecture reviews.

Provides a structured reference covering the 100 most critical web vulnerabilities, including definitions, root causes, impacts, and mitigation guidance aligned with OWASP categories to support assessments and remediation planning.

Provides a structured reference of the 100 most critical web application vulnerabilities organized by category, helping security teams identify attack vectors, assess impacts, and follow remediation guidance during audits or penetration tests.

torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.