Skill UI

This comprehensive skill provides full programmatic control over the entire work order lifecycle within the MaintainX platform. Users can create new work orders with detailed metadata, programmatically transition their status (Open, In Progress, On Hold, Completed), query existing tasks, and attach documentation or comments. It is ideal for automating complex maintenance operations and ensuring end-to-end operational visibility.

Manages complete database testing workflows by generating realistic test data, wrapping tests in automatic transactions, and validating schema integrity to keep migrations and interactions reliable.

This skill guides the integration of Model Context Protocol (MCP) into Claude Code plugins. It enables plugins to securely connect to external services, APIs, databases, and file systems by exposing capabilities as structured tools. Supports multiple connection methods (stdio, SSE, HTTP, WebSocket) and handles complex authentication flows like OAuth and token-based access for robust tool access.

Automates Mistral AI workflows through the Composio MCP integration, letting teams upload files for fine-tuning, batch jobs, OCR, manage RAG document libraries, monitor tuning jobs, and retrieve or catalog data for cross-app pipelines.

A comprehensive workflow for advanced AI applications using Mistral AI. This guide covers generating text and code embeddings (1024 dimensions), implementing semantic search via cosine similarity, and building robust RAG pipelines. It also demonstrates advanced tool augmentation using function calling, allowing LLMs to interact with external APIs and databases.

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Guides writing Python code inside n8n Code nodes, covering recommended use cases, helper data access patterns, Beta vs Native modes, and webhook/data handling rules so you can safely script workflow transformations with Python.

Comprehensive expert guide for using n8n-MCP tools to find nodes, validate configurations, manage workflows, deploy templates, and leverage data-table helpers with consistent patterns.

Catalogs proven n8n workflow architectural patterns for webhook handling, APIs, database syncing, AI agents, and scheduled tasks, helping teams pick structures, plan nodes, and handle triggers, data paths, and error strategies during workflow design and troubleshooting.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.