Skill UI

Automates importing live Azure environments into Terraform by discovering subscriptions, groups, or resource IDs, mapping dependencies, and generating AVM-based IaC files ready for validation and planning.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Generates a complete, compilable xUnit integration test project structure for validating complex database migrations, specifically from Oracle to PostgreSQL, within .NET solutions. This scaffolding includes a transaction-rollback base class and a seed data manager, ensuring a robust environment for writing migration integration tests without requiring test cases. Ideal for setting up testing infrastructure before migration validation.

A comprehensive guide covering best practices for Terraform and OpenTofu. It provides structured advice on module hierarchy, directory organization, naming conventions, and advanced testing strategies (including native testing and policy as code). Use this skill when designing, refactoring, or implementing robust Infrastructure as Code (IaC) pipelines and multi-environment deployments.

A comprehensive automation toolkit designed to manage the entire Vercel deployment lifecycle and associated infrastructure. Users can programmatically monitor, trigger, and manage deployments, configure custom domains and DNS records, manage environment variables across multiple targets, and oversee project settings and project listings. Essential for implementing robust CI/CD pipelines and managing cloud infrastructure.

Transforms you into a 10x web engineer, guiding work across HTML, CSS, JavaScript, web APIs, HTTP/HTTPS, security, performance, accessibility, and infrastructure so you can deliver standards-compliant, high-quality front- and back-end solutions.

Ginkgo Cloud Lab provides remote access to an advanced autonomous lab infrastructure, enabling users to submit and manage complex molecular biology protocols. It supports diverse applications including protein expression and purification (cell-free, E. coli, Pichia), quantitative assays (HiBiT, A280, LabChip), nucleic acid synthesis (IVT mRNA/circRNA), and characterization techniques (Thermal Shift, Echo-MS, SPR). The platform features an AI agent (EstiMate) for custom workflow feasibility and pricing.

LaminDB is an open-source, lineage-native lakehouse specifically designed for complex biological datasets and models. It provides unified capabilities for querying, tracking data lineage, validating schemas, and ensuring FAIR compliance across diverse data types (e.g., AnnData, Parquet, Zarr). Use this skill when managing multi-modal genomic data, tracking computational workflows (like Nextflow/Snakemake), or building robust, reproducible data infrastructure in bioinformatics.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.

Open Notebook is a self-hosted, open-source alternative to major AI research tools. It allows users to ingest diverse content—including PDFs, videos, and web pages—and organize them into private notebooks. Key features include context-aware AI chat, automated summarization, multi-speaker podcast generation, and full programmatic access via a comprehensive REST API. By running on your own infrastructure, it ensures complete data privacy and supports 16+ AI providers.

A robust, production-ready architecture pattern for integrating with multiple Adobe APIs, including Firefly Services, PDF Services, and I/O Events. This pattern enforces a layered structure (API -> Service -> Client -> Infrastructure), ensuring clean separation of concerns, centralized authentication, robust error handling boundaries, and scalable workflow orchestration for enterprise applications.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.