Skill UI

This guide provides comprehensive instructions for setting up robust CI/CD pipelines for Instantly.ai integrations using GitHub Actions. It covers unit and integration testing against both a mock server and the live API, includes API scope validation scripts, and details the deployment process for webhook receivers (e.g., to Cloud Run). Use this when ensuring continuous validation and reliable deployment of your Instantly API code.

An expert guide for building scalable, cloud-native enterprise applications using the latest Java 21+ features, including Virtual Threads and Pattern Matching. Deep mastery of the Spring Boot 3.x ecosystem, Spring Cloud, and microservices architecture. Covers full development lifecycle, from JVM performance tuning (GraalVM, ZGC) to robust testing (Testcontainers) and secure DevOps practices (Kubernetes, CI/CD).

A comprehensive guide for setting up a local development loop to test AI agent integrations. This skill teaches developers how to build and debug webhook receivers, callback handlers, and associated application code that interacts with a cloud-hosted AI agent (like Lindy). It requires using tunneling services (e.g., ngrok) to expose local endpoints for receiving webhooks and simulating the entire agent-to-application data flow.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

Automates the collection of comprehensive diagnostic evidence for PostHog support tickets. This bundle gathers runtime environment details (Node, Python versions), SDK versions, API connectivity status to US/EU clouds, feature flag settings, event flow testing results, and redacts sensitive environment variables. It provides a structured, support-ready archive for rapid troubleshooting.

This central skill specializes in performing comprehensive threat modeling using STRIDE and calculating risk via DREAD scoring. It guides users through defining scope, identifying threats, and planning mitigations for new architectures. Crucially, it acts as a security router, directing specialized requests—such as pen-testing, incident response, cloud posture checks, and AI security reviews—to the appropriate dedicated skill.

This skill validates web applications across diverse browsers (Chromium, Gecko, WebKit) and real mobile devices. It supports both local testing using Playwright and scalable cloud testing via major platforms like BrowserStack, Sauce Labs, and Kobiton. Use it when ensuring consistent UI/UX, correct CSS rendering, and functional JavaScript behavior regardless of the target environment or device type.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

A comprehensive guide demonstrating the full lifecycle of GPU resource utilization on Vast.ai. This includes searching for available hardware using CLI and REST APIs, provisioning a dedicated GPU instance (e.g., using PyTorch images), connecting via SSH, running deep learning workloads, and finally, securely destroying the instance to stop billing. Ideal for testing cloud integration or managing ML compute pipelines.

This guide establishes a fast, reproducible local development workflow for GPU workloads using Vast.ai. It covers key practices such as testing Docker images locally (in CPU mode), mocking external APIs like Vast.ai, and implementing robust unit and integration tests. This is essential for minimizing cloud GPU costs and accelerating the iteration cycle for complex AI/ML projects.

Configure Playwright projects to run on BrowserStack’s cloud grid, add dedicated scripts, fetch builds and sessions, and report per-browser results so you can automate cross-browser/device testing with tunnel support.

A comprehensive guide covering methodologies for conducting authorized penetration testing across major cloud platforms, including AWS, Azure, and GCP. It details the shared responsibility model, techniques for exploiting IAM misconfigurations, testing for SSRF vulnerabilities to cloud metadata services, and utilizing industry tools like Pacu and ScoutSuite. Findings are mapped to the MITRE ATT&CK Cloud matrix for robust reporting.