Skill UI

Automates comprehensive regression testing for all enabled cloud Large Language Model (LLM) providers. This includes verifying core connectivity, standard chat completion, structured JSON output, and Server-Sent Events (SSE) streaming capabilities. Essential for ensuring the reliability and stability of multi-cloud AI integrations in a CI/CD pipeline.

This script automates the collection of comprehensive diagnostic evidence for Cohere API v2 troubleshooting. It gathers environment details (Node/Python versions), SDK versions, API connectivity tests, and detailed request/response logs, packaging everything into a single, support-ready bundle for efficient issue resolution.

Implements a robust, multi-tenant authorization layer for Cohere API v2. This solution enforces Role-Based Access Control (RBAC), manages individual API keys per team, and implements per-team usage limits and daily budget tracking. It is essential for large-scale applications requiring strict resource isolation and cost governance across multiple internal services.

A comprehensive guide providing minimal working examples for core Cohere API v2 endpoints, including Chat completion, text embedding, and search reranking. This resource details basic setup, usage patterns, and includes runnable code examples in both TypeScript and Python, making it perfect for new AI integrations or learning advanced LLM functionality.

Automate the entire deployment lifecycle for ML inference services on CoreWeave GPU cloud. This workflow integrates unit testing, container image building (to GHCR), and rolling deployment to CoreWeave Kubernetes clusters via GitHub Actions. Ideal for ensuring stable, high-performance deployment of GPU-dependent models. Key features include validating GPU manifest requests and managing deployments using standard Kubernetes APIs.

A comprehensive guide to diagnose and resolve common CoreWeave infrastructure errors. It covers issues related to GPU scheduling, Pod status (e.g., Pending), CUDA Out of Memory errors, NCCL timeouts, and Kubernetes resource allocation within AI/ML deployment pipelines.

A comprehensive guide and set of tools for establishing a local development workflow tailored for CoreWeave GPU deployments. This includes steps for building Docker containers, validating Kubernetes YAML manifests using dry-run, and managing the full deployment lifecycle for AI inference services before pushing to the cloud.

A comprehensive utility designed for managing CoreWeave cloud infrastructure migrations. It facilitates upgrading between GPU generations (e.g., A100 to H100), updating CUDA versions, and handling Kubernetes resource changes. The tool uses K8s APIs to audit deployments, detect deprecated instances, and execute controlled rollbacks.

A comprehensive security auditing framework designed to systematically detect sensitive information disclosure from Large Language Models (LLMs). It uses multi-phase, escalating dialogue probes to test for leaks such as system prompts, API keys, PII, and internal configurations. Essential for red teaming and rigorous model security assessment.

Deprecated compatibility redirect pointing to the canonical Aurora DSQL skill in awslabs/agent-plugins. Use for DSQL, distributed SQL schema, migration, query, auth, performance, or app development requests.

Implement professional text-to-speech generation and advanced voice cloning using the ElevenLabs API. This comprehensive workflow supports standard text-to-speech conversion, advanced voice customization (stability, similarity boost), and instant voice cloning from audio samples. It also includes WebSocket streaming for real-time applications like chatbots or live narration, making it ideal for building sophisticated, high-quality audio features.

This guide provides comprehensive techniques for optimizing ElevenLabs Text-to-Speech (TTS) performance. Learn how to reduce latency and maximize throughput by strategically selecting models (e.g., Flash vs. v3), implementing streaming endpoints (HTTP or WebSocket), and choosing optimal audio formats. It is essential for real-time conversational AI, IVR systems, and high-volume content generation.