Skill UI

Enables Claude to configure neural network architectures through the neural-network-builder plugin, creating or modifying models and defining layers, activation functions, and training parameters whenever network design or tuning is requested.

Defines how to automate Codeinterpreter tasks through Composio’s Rube MCP by first discovering current tool schemas, managing the Codeinterpreter connection, and executing schema-compliant tool calls while reusing sessions and honoring memory parameters.

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

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.

This comprehensive guide helps developers safely migrate between different versions of the Kling AI video generation model (e.g., v1.x to v2.x). It details breaking changes, parameter differences, feature availability matrices (including native audio and camera control), and provides code examples for parallel A/B testing and robust rollback strategies, ensuring seamless API adoption.

This skill provides a comprehensive framework for configuring Mistral AI across distinct environments (development, staging, and production). It manages environment-specific secrets (using GCP, AWS, or Vault), defines critical parameters like default models, rate limits, and feature flags, ensuring secure, isolated, and reliable deployments for AI services in multi-stage pipelines.

This guide provides a comprehensive quickstart for integrating with OpenRouter, a unified endpoint for accessing multiple large language models (LLMs). It includes examples in cURL, Python, and TypeScript, demonstrating how to send chat completion requests, handle complex JSON responses, manage model parameters, and calculate usage cost across various providers (e.g., Llama, GPT-4o, Claude). Ideal for developers testing API connectivity or building multi-model applications.

Covers HuggingFace PEFT workflows for LoRA/QLoRA-based LLM fine-tuning, enabling sub-1% parameter updates on limited GPUs plus adapter serving tips.

Meta’s Prompt Guard is an 86M-parameter classifier that spots prompt injections and jailbreak attempts before they reach LLMs, filtering user input, third-party data, or RAG documents with multilingual support and sub-2ms latency for fast deployments.

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

Automates the setup of ML experiment tracking with MLflow or W&B, configures the environment, initializes servers, and supplies snippets for logging parameters, metrics, and artifacts so runs stay reproducible and easy to compare.