Skill UI

Automatically scans code for reflected, stored, and DOM-based XSS risks across HTML, JavaScript, CSS, and URLs, providing reports and safe proof-of-concept payloads for use in reviews, audits, or pre-deployment checks.

This skill covers the full lifecycle of deploying machine learning models into production environments. It focuses on establishing robust MLOps pipelines, including model deployment workflows (e.g., Docker, Canary releases), feature store management (Feast), and comprehensive drift monitoring. Furthermore, it details advanced integration patterns for Large Language Models (LLMs) and building Retrieval-Augmented Generation (RAG) systems, emphasizing operational stability, performance validation, and cost optimization.

This guide details how to configure the Speak language learning platform across multiple environments (dev, staging, production). It is essential for implementing robust SaaS deployments, ensuring that separate API keys, mock modes, and environment variables are correctly managed for seamless development and operational management.

Automates guidance for streaming inference setup, covering best practices in ML deployment, serving, monitoring, and production-ready configurations whenever you ask about streaming inference patterns or implementation.

Guides TensorFlow Serving setup with step-by-step ML deployment orchestration, enforcing best practices, ready-to-run configurations, and output validation for production inference services.

Assists with torchscript exporter tasks for ML deployment, offering automated guidance, code/config generation, and validation against industry standards whenever torchscript exporters are mentioned.

Automates generating TorchServe deployment configurations with guided best practices for model serving, MLOps pipelines, inference, and production monitoring so ML deployment workflows stay consistent and reliable.

Enables Claude to manage AI/ML model versions through the model-versioning-tracker plugin, automating logging, lineage tracking, performance summaries, and version retrieval whenever users request registry or deployment support.

Automates Triton inference config guidance for ML deployment, covering serving, MLOps pipelines, monitoring, and production-ready configurations aligned with best practices.

A comprehensive guide for deploying Machine Learning training jobs and inference services on the Vast.ai GPU cloud. This skill covers the entire deployment lifecycle, including optimizing Docker images for performance, creating automated provisioning scripts using the Vast.ai CLI, implementing robust data transfer strategies (SCP, rsync), and performing post-deployment health checks.

A comprehensive checklist for deploying large-scale, production-ready GPU workloads on Vast.ai. It covers critical lifecycle stages including account authentication, instance validation, data encryption, persistent checkpointing strategies, fault tolerance for spot instances, robust monitoring (utilization & cost), and strict cost controls, ensuring reliable and stable ML/AI deployments.

Configure Vercel's advanced role-based access control (RBAC) for enterprise environments. This guide covers setting up granular team roles, managing project-level access groups, integrating SAML SSO for secure authentication, and enabling detailed audit logging. Use this when implementing strict security protocols and enforcing the principle of least privilege across your team's deployments and resources.