Skill UI

Guide for automating Agentql tasks through Composio’s Rube MCP toolkit, covering prerequisites, setup, discovery, execution, and error avoidance so you can safely run workflows using the latest schemas.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

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

A comprehensive guide to configuring and maximizing the potential of Claude Code, an advanced agentic coding tool. This resource details best practices, setup templates (CLAUDE.md), advanced prompting techniques (e.g., thinking keywords), and debugging strategies for building reliable, complex software autonomously.

Comprehensive guide for configuring Bring Your Own Key (BYOK) for major AI providers, including OpenAI, Anthropic, Google Gemini, Azure, and custom endpoints. This allows users to bypass default quotas, manage costs directly with the source provider, and utilize a wide range of specialized models within the Cursor IDE. It covers setup steps, security best practices, and cost monitoring strategies.

Guides automated distributed training setups with step-by-step practices, production-ready configs, and validation checks for ML training workflows.

Guides ML engineers through dataset prep, PEFT strategy selection, LoRA/QLoRA adapter setup, training monitoring, and deployment checks for practical LLM fine-tuning, quantization, and adapter merge workflows.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

Automates GPU resource optimization for ML deployments by guiding best practices, generating production-ready configurations, and validating setups for model serving, MLOps pipelines, and monitoring.

Execute complex and compute-intensive workloads directly on fully managed Hugging Face cloud infrastructure. This skill eliminates the need for local setup, allowing users to run jobs using cloud CPUs, GPUs, or TPUs. Ideal for large-scale data processing, batch inference, reproducible model training, and scheduled automation tasks, while persisting results to the Hugging Face Hub.

A comprehensive guide demonstrating how to generate your first AI image using the Ideogram API. It provides runnable examples in cURL, TypeScript, and Python, covering both legacy and V3 endpoints. Users will learn essential API parameters, such as prompt structuring, style types, and aspect ratios, making it ideal for initial integration testing and setup.