Skill UI

A comprehensive guide to optimizing CAST AI's core performance metrics, including node provisioning speed, autoscaler responsiveness, and API call efficiency. This skill covers techniques like configuring headroom, selecting optimal instance families, tuning evictors, and implementing caching for multi-cluster dashboards to ensure high availability and cost efficiency in cloud environments.

A quick reference guide for diagnosing and resolving common runtime and deployment errors encountered on the Fly.io platform. Covers issues such as health check failures, image build failures, machine startup issues, and connection refusals, providing specific diagnostic commands and configuration fixes.

A comprehensive guide to optimizing cloud spending on Fly.io. Learn advanced strategies for cost reduction, including implementing auto-stop/suspend features, correctly right-sizing Virtual Machines (VMs), managing and cleaning up orphaned volumes, and utilizing proper billing monitoring tools. Essential for efficient cloud resource management.

Provides a comprehensive reference architecture for deploying highly scalable, low-latency applications on Fly.io. It details best practices for multi-region web tiers, achieving high availability with Postgres read replicas, implementing Redis caching, managing background workers, and securing connectivity via private internal networking for robust production systems.

A comprehensive solution for monitoring CoreWeave cluster events and GPU workload status using webhooks. It tracks critical lifecycle events such as pod readiness, job completion, volume attachment, and node health. Essential for building automated scaling, alerting, and recovery pipelines for large-scale GPU-accelerated inference and training workloads.

This skill guides the secure configuration of Figma API access across development, staging, and production environments. It details best practices for managing separate API tokens (PATs), file keys, and cache settings. The focus is on implementing robust environment guards and utilizing secret management solutions to prevent cross-environment data leakage and accidental operations.

This comprehensive bundle collects all necessary diagnostic evidence for Fly.io applications, including machine status, application logs, volume metadata, and network diagnostics. It is crucial for generating detailed support tickets and troubleshooting complex infrastructure issues such as stuck deployments, boot failures, or edge networking problems.

This guide provides methods for robust monitoring and automation tailored for services deployed on Fly.io. It covers key areas including polling the Machines API to detect state changes, implementing comprehensive health check endpoints, processing structured logs using tools like `jq`, and automating deployment notifications within CI/CD pipelines. Essential for achieving high availability and reliable operations.

This guide provides a comprehensive strategy for configuring Klaviyo across development, staging, and production environments. It covers best practices for using separate API keys, implementing robust environment detection logic (like reading NODE_ENV), and utilizing secret management solutions (AWS/GCP/Vault). Crucially, it teaches how to implement 'guards' to prevent accidental real-world actions, such as sending live emails or deleting data, in non-production environments, ensuring data integrity and smooth CI/CD deployment.

This comprehensive skill guides users through the entire Fluid storage extension lifecycle within KubeSphere. It allows for installing, upgrading, and uninstalling the Fluid extension, and managing various data resources. Users can generate YAML manifests for Datasets and specialized runtimes (Alluxio, JuiceFS, Thin), perform data loading/cache warming, scale operations, and troubleshoot issues using both kubectl and KubeSphere APIs.

This skill provides a deep dive into the architecture of low-level keyloggers. It covers techniques such as kernel driver hooks, using WH_KEYBOARD_LL, ETW-based input capture, and analyzing user-mode vs. kernel-mode approaches. Use this methodology to understand input capture mechanisms, research EDR evasion methods, or analyze malware architecture for advanced security threat detection.

This skill provides a comprehensive checklist and detailed methodology for detecting HTTP Request Smuggling vulnerabilities. It covers various desynchronization types (CL.TE, TE.CL, TE.TE), including HTTP/2 and modern variants. Use this when penetration testing multi-server architectures, proxies, or load balancers to identify potential security bypasses, unauthorized access, or cache poisoning risks.