Skill UI

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Provides automated guidance for setting up canary deployments in ML production environments, covering model serving, MLOps pipelines, monitoring, and configuration validation.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

Guides you through installing the Fireflies.ai SDK/CLI, setting API credentials via environment variables or .env files, and verifying the connection so you can securely integrate Fireflies.ai into new Node.js or Python projects.

Sets up Groq LLM API clients for development, staging, and production tiers with tailored models, retries, and rate-limit handling so deployments stay aligned with each environment’s speed and cost profile.

Guides installing the Ideogram SDK/CLI and configuring API credentials for new integrations, covering environment variables, verification, and troubleshooting around authentication.

Guides you through generating Kling AI API keys, configuring environment variables, and validating authentication so you can start integrating Kling AI video generation without auth issues.

Collects environment details, trace logs, and minimal reproduction scripts for LangChain issues, packaging them into a debug bundle that accelerates bug reports, diagnostics, and support conversations.

Provides Lindy AI agents with a step-by-step diagnostics suite for environment info, API checks, agent state, run history, and support bundle generation when investigating issues.

llama.cpp is a pure C/C++ library designed for efficient LLM inference, optimized to run on resource-constrained environments. It excels in scenarios where high-end NVIDIA GPUs (CUDA) are unavailable, making it ideal for Apple Silicon Macs, AMD/Intel GPUs, or edge deployment devices like Raspberry Pi. It supports GGUF quantization formats, enabling significant memory reduction and speed improvements for local, cross-platform AI applications.

Guide for installing the Mistral SDK/CLI, setting up API keys via environment variables or dotenv, and verifying connectivity across Node.js and Python so integrations can securely consume chat, embedding, and function-calling endpoints.