Skill UI

Build automated pipelines to respond to AWS GuardDuty findings. This solution uses EventBridge to detect high-severity threats and triggers a Lambda function. The function automatically quarantines compromised resources (e.g., EC2 instances), creates forensic EBS snapshots, and sends detailed notifications via SNS, drastically reducing Mean Time To Respond (MTTR). Ideal for proactive cloud security posture management.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).

A comprehensive tool for managing Lark/Feishu approval workflows. It allows users to query pending or completed tasks, view detailed instance progress, and perform essential actions such as approving, rejecting, transferring, adding signers, or following up on requests. Ideal for automating manual approval steps in a business process.

A skill for creating, editing, validating, syncing, and troubleshooting n8n workflows using n8n-as-code. Manages workspace environments, local instances, tunnels, and workflow operations through command-line interfaces. Supports environment configuration, API key management, and workflow lifecycle management.

Provides advanced control over browser debugging protocols (CDP). Use this skill when the user needs to connect to an external, running Chrome instance, scan local CDP ports, or explicitly launch the browser with a fixed debug port. It is specifically designed for complex automation scenarios where multiple tools or agents need to attach to the browser instance.

A comprehensive reference architecture guide for deploying and managing CAST AI across multiple Kubernetes clusters (Dev, Staging, Prod). It details best practices for infrastructure provisioning using Terraform, focusing heavily on cost optimization techniques such as autoscaling, spot instance management, and environment-specific resource policies. Ideal for setting up team standards and ensuring reliable, cost-effective deployments.

This workflow guides users through configuring CAST AI's autoscaler policies to achieve optimal cost management and resource utilization within a Kubernetes cluster. It covers enabling spot instances, setting node downscaler/evictor rules, defining cluster limits, and creating specialized node templates (e.g., for GPU or spot workers) using Terraform. Use this when implementing Phase 2 automation or optimizing cloud spend.

A comprehensive guide and code pattern for configuring ClickHouse instances across development, staging, and production environments. It emphasizes best practices for infrastructure-as-code (IaC), secure secrets management (integrating AWS Secrets Manager, Vault, etc.), and ensuring environment-specific settings are correctly applied for reliable data analytics pipelines.

This skill uses the OCI Usage API and SDK to programmatically track spending across compartments, services, and shapes. It allows users to query granular cost data, set detailed budget alerts (e.g., 80%, 95%, forecast), and identify cost optimization opportunities like utilizing Always Free tiers or preemptible instances, ensuring proactive and precise cloud cost management.

A comprehensive guide to launching, listing, and managing Oracle Cloud Infrastructure (OCI) compute instances using Python. This guide specifically addresses the common 'Out of host capacity' error when using Always Free ARM shapes, implementing a robust retry loop to ensure successful deployment. Users can learn the full lifecycle management, from initial connection setup to instance state polling.

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).