Skill UI

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.

This skill provides advanced expertise in Infrastructure as Code (IaC) using Terraform and OpenTofu. It covers designing robust, reusable modules, managing complex multi-cloud state backends, and implementing enterprise-grade CI/CD pipelines. It specializes in governance, Policy-as-Code, and achieving GitOps compliance for full infrastructure automation.

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

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.

This comprehensive workflow provides a complete cycle of quality assurance for software development. It covers every stage from defining the test strategy (TDD, Unit Testing) through integration, E2E testing, browser automation, performance profiling, security scanning, and final code review. Use this when setting up robust testing infrastructure or ensuring production-ready software quality.

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.

A comprehensive checklist designed to ensure TwinMind integrations are deployed securely, stably, and compliantly in a production environment. It covers critical best practices across multiple domains, including authentication, data encryption (PII, GDPR), robust infrastructure setup (auto-scaling, load balancing), advanced monitoring (metrics, tracing, alerting), and full CI/CD deployment workflows. Use this when preparing for go-live or conducting production audits.

Upstash QStash expert handles serverless message queues, scheduled HTTP jobs, retries, deduplication, and callback tracking so you can deliver asynchronous tasks without managing infrastructure.

Validates PCI DSS compliance by running the pci-dss-validator plugin on codebases or infrastructure configs, then reports violations and remediation steps for developers, security engineers, and compliance officers.

Collects comprehensive diagnostic evidence for troubleshooting Vast.ai GPU instances. This bundle gathers account status, lists all running instances, retrieves recent logs, performs remote GPU diagnostics (e.g., nvidia-smi, CUDA checks), and verifies API connectivity. It is essential for creating detailed support tickets when encountering persistent infrastructure issues.

Automates Vault Secrets Integrator guidance for DevOps Advanced tasks, covering kubernetes, terraform, CI/CD, monitoring, and infrastructure as code best practices to deliver validated configs and code.