Skill UI

Automates updating Azure Verified Modules referenced in Bicep files to their latest released versions, guiding scan, version comparison, documentation checks, and validation workflows while limiting reports to non-breaking changes.

Author, review, simulate, and troubleshoot Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots, covering deck and labware setup, pipettes, modules, runtime parameters, liquid handling, and safe deployment.

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining flexibility. It automates training workflows, supports multi-GPU/TPU orchestration, and implements best practices for neural network training. Features include LightningModules, Trainers, data pipelines, callbacks, logging (W&B, TensorBoard), and distributed training strategies like DDP and FSDP for scalable training.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

A comprehensive guide and setup for establishing a fast, reproducible local development loop for web scraping projects using Bright Data. It includes configuring proxy modules, implementing response caching to save credits, and setting up mocked test suites (using Vitest) for robust, isolated testing without requiring live proxy access or consuming resources.

This guide sets up a robust local development loop for integrated Kubernetes infrastructure using CAST AI. It allows developers to test autoscaler policies, validate Terraform modules, and measure potential cloud cost savings in a dev environment before deploying to production. This is essential for building cost-aware and efficient CI/CD pipelines.

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

This comprehensive workflow manages the end-to-end life cycle of sales readiness training on the MindTickle platform. Users can create complex courses containing mixed media (videos, documents, quizzes). It allows assigning courses to teams with due dates, tracking learner progress and quiz scores via analytics API, and updating content modules as business needs evolve.

This guide provides a minimal working example demonstrating core MindTickle API functionality. It shows how to programmatically create structured training modules (including videos and quizzes), assign them to specific teams or users with deadlines, and track user progress and overall completion rates using analytics endpoints. It is ideal for integrating Learning Management System (LMS) functionality into internal corporate tools and automating onboarding processes.

This tool provides a robust local development workflow (dev loop) for integrating with the MindTickle sales enablement and readiness API. It simulates critical data points—such as training modules, user progress, and coaching data—using mock services. This allows developers to build and test complex sales readiness dashboards and features rapidly and reliably, by toggling between a mock mode for fast iteration and a sandbox mode for real-world validation against the MindTickle platform.

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 guide details how to scaffold complete, structured exercise directory setups, including mandatory sections for problems, reference solutions, and conceptual explainers. It outlines precise naming conventions, required files (like readme.md and main.ts), and a robust workflow that ensures the setup passes linting checks. It is ideal for creating new course modules, generating exercise stubs, or structuring large-scale educational curricula.