Skill UI

This skill provides comprehensive API calls using `curl` to interact with the CAST AI platform. It allows users to list connected Kubernetes clusters, retrieve detailed cost savings reports, and inspect the inventory of managed nodes. It is essential for verifying connectivity, auditing cloud spending, and monitoring resource allocation after onboarding.

This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

This guide provides a comprehensive walk-through of making the first REST API calls to Figma. Users learn how to fetch core file metadata (name, version, pages) and traverse the complex document structure (nodes, frames, components). It's ideal for developers starting a new Figma integration, setting up API connectivity tests, or understanding the fundamental relationships between design elements within a Figma file.

This skill provides a comprehensive guide to building robust LangGraph 1.0 StateGraphs in Python. It covers defining typed TypedDict states with reducers, writing deterministic nodes, and defensively wiring conditional edges. It addresses common pitfalls such as silent graph termination, incorrect state updates using reducers, proper management of recursion limits, and handling evolving checkpoint schemas for reliable LLM workflow orchestration.

Master advanced patterns for composing LangGraph subgraphs, which is essential for building robust, multi-agent systems like planner-executors or hierarchical teams. This skill addresses critical issues such as shared state contract propagation (P21) and callback scoping (P28). Learn to manage state across independent graph components using sophisticated dispatch patterns: compiled nodes, Send fan-out, and Command bubble-up.

This guide provides comprehensive strategies for scaling datasets that exceed the capacity of a single node. It covers various architectural patterns, including multi-tenancy with payload partitioning and sliding time window management. We detail the full scaling progression, from optimizing a single node (Vertical Scaling) to distributing data across multiple nodes via sharding (Horizontal Scaling), ensuring reliable handling of massive, growing datasets.

This guide provides comprehensive guidelines for performing vertical scaling on Qdrant instances. Vertical scaling involves upgrading the computational resources (CPU, RAM, or disk) of existing nodes rather than adding new nodes. It is the recommended first step when single nodes experience resource exhaustion (e.g., RAM usage > 80%, CPU saturation). The guide covers sizing guidelines, best practices for cloud deployment, and clearly defines the thresholds when horizontal scaling becomes necessary.

qzcli is a powerful command-line interface (CLI) tool designed for managing complex GPU compute jobs on the Qizhi platform. Modeled after industry standards like kubectl, it allows users to log in, discover available resources (nodes, workspaces), submit interactive or highly complex batch jobs (including matrix sweeps), and monitor job status efficiently. It is essential for handling high-performance computing (HPC) and distributed machine learning training workflows.

This skill provides comprehensive operations for managing NodeGroup resources. It allows users to query, create, update, delete, and manage bindings for nodes, namespaces, and workspaces within the edgewize nodegroup environment. Ideal for infrastructure automation, troubleshooting, and API-driven cluster management.

A comprehensive guide detailing attack methodologies for Z-Wave smart home networks. Techniques covered include sniffing during inclusion (S0/S2), exploiting key derivation flaws, performing replay attacks on unauthenticated nodes, and crucial hub compromise (pivoting) to gain full control over the entire mesh network. Essential reading for IoT penetration testing.

kube-bench is an open-source tool that validates the compliance and security posture of Kubernetes clusters. It rigorously checks control-plane components, worker nodes, and cluster policies against the comprehensive CIS Kubernetes Benchmark. It reports findings (PASS/FAIL/WARN) and includes specific remediation guidance, making it essential for compliance auditing and hardening efforts.