Skill UI

This skill guides users through implementing comprehensive security measures for Vercel deployments. It covers best practices for secret management (using sensitive environment variables), configuring critical HTTP security headers (such as CSP and HSTS), protecting preview environments, and enforcing robust access control for API routes and deployment tokens.

This comprehensive guide provides a step-by-step workflow for safely upgrading a full-stack application deployed on Vercel. It covers migrating Vercel CLI versions, updating Node.js runtimes, and handling major Next.js framework version changes. The guide emphasizes breaking change detection, schema updates, and implementing robust preview testing and rollback strategies for a smooth deployment process.

A comprehensive guide for implementing secure and reliable webhook endpoints to handle Vercel deployment lifecycle events. This skill covers signature verification (HMAC) to ensure security, processing key events (e.g., deployment.ready, deployment.error), and implementing idempotency to prevent duplicate actions. Ideal for building automated CI/CD integrations, status notifications, or custom project hooks that react to deployment changes in real-time.

Enforces a verification-first routine: identify the proving command, rerun it fully, inspect the output, and cite its evidence before claiming tests, builds, or tasks are complete; keeps handoffs honest and avoids false satisfaction.

Guides training large language models with Volcano Engine's verl library, covering PPO/GRPO workflows, distributed backends, multi-turn rollouts, reward configs, critic-enabled PPO, and production deployments using Ray workers.

This skill provides a comprehensive guide to implementing robust API versioning strategies, supporting methods like URL path, custom headers, and query parameters. It covers building backward compatibility layers, adding standard deprecation notices (e.g., Sunset Header), and utilizing OpenAPI specifications to detect breaking changes, ensuring stable API evolution over time.

Automates Vertex AI's multimodal media workflows—video understanding, image/audio generation, and marketing campaign assets—so teams can produce cross-channel creatives from a single prompt.

Provides automated guidance for designing, validating, and deploying Vertex AI pipelines within Google Cloud, covering compute, storage, BigQuery, and GCP-specific services for production readiness.

This skill thoroughly inspects and validates Vertex AI Agent Engine deployments. It assesses critical components such as the Code Execution Sandbox, Memory Bank, A2A protocol compliance, security posture, and performance metrics. The tool generates weighted production-readiness scores (0-100%) and provides detailed, actionable recommendations to ensure the deployment is robust, secure, and ready for production use.

Vexor is a powerful command-line interface designed for advanced semantic search across local files. By utilizing vector embeddings and integrating with large language models (like Claude/Codex), it allows users to search files based on meaning and context, rather than just exact keywords. This is ideal for large codebases or document repositories where traditional keyword search methods are insufficient.

Downloads YouTube and other platform videos for offline viewing, editing, or archiving, letting you batch download, pick resolutions, and preserve metadata in MP4, WebM, or audio-only formats.

Automates spoken-word video editing by transcribing footage, detecting mispronunciations or pauses with AI, generating review pages, and surfacing actionable deletion lists for user approval before trimming.