Skill UI

Monitors Nx Cloud CI pipelines by running the ci-watcher subagent, polling execution status, and applying self-healing fixes or rerun requests while honoring user overrides and Nx Cloud connection requirements.

This guide provides comprehensive instructions for deploying applications powered by the Perplexity Sonar API. It covers setup for major cloud platforms like Vercel, Google Cloud Run, and Docker. Users can deploy production-ready endpoints, manage required secrets, implement edge functions, and utilize caching mechanisms (e.g., Redis) to ensure high availability and performance in production 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.

A comprehensive guide for deploying PostHog analytics platform across diverse production environments. Covers advanced configurations for Next.js/Vercel (including reverse proxy for ad blocker bypass and edge functions for server-side capture), self-hosted setups using Docker, and deployment patterns for Google Cloud Run. Ensures robust, reliable user behavior tracking regardless of the hosting platform.

This skill simulates an expert cybersecurity auditor specializing in DevSecOps methodologies and comprehensive security assurance. It guides users through running detailed security audits, performing threat modeling, and assessing vulnerabilities (e.g., IDOR). Capabilities cover SAST, DAST, cloud security posture, zero-trust architectures, and compliance adherence to major frameworks like GDPR, HIPAA, and PCI-DSS, ensuring security is integrated throughout the entire SDLC.

Guides GCP service account management requests with automated best practices, step-by-step help, production-ready code/configuration, and validation across compute, storage, BigQuery, Vertex AI, Cloud Run, and Firebase workflows.

This guide outlines the end-to-end process for deploying Speak language learning features to modern cloud environments, including Vercel, Cloud Run, or containerized setups. It covers initial configuration, implementing the core logic using TypeScript, and performing production verification. Use this skill when setting up a new Speak integration in a production environment or managing the platform's deployment lifecycle.

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.

This comprehensive workflow guides users through the end-to-end process of utilizing cloud-based GPU computing resources on Vast.ai. It covers searching for ideal GPU offers based on specs and price, provisioning an instance, running complex training or inference jobs via SSH, collecting model artifacts, and crucially, destroying the instance to prevent unexpected billing. Ideal for deep learning training and research.

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 comprehensive runbook provides rapid, structured procedures for incident response on Vast.ai cloud GPU instances. It covers common failure scenarios like spot preemption, training job crashes, and GPU hardware issues. Users can follow the steps for triage, mitigation, recovery, and postmortem analysis to minimize downtime and ensure computational continuity.

This skill provides comprehensive monitoring capabilities for GPU instances running on Vast.ai. It collects key metrics including GPU utilization, instance uptime, temperature, and accumulated costs. Use this when setting up operational monitoring dashboards, configuring critical alerts (e.g., idle GPUs, high temperature, budget overruns), or automating resource usage tracking in a cloud computing environment.