Skill UI

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

This guide details how to establish comprehensive observability for LLM operations using Langfuse. It covers setting up Prometheus metrics (tracking traces, generation duration, and token usage) and visualizing them in Grafana dashboards. Ideal for monitoring application health, optimizing costs, and tracking performance over time.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

Weights & Biases (W&B) automates experiment logging, real-time dashboards, hyperparameter sweeps, artifact tracking, and model registry/versioning for ML teams using PyTorch, TensorFlow, or HuggingFace workflows.

Connects to the MAXIA AI-to-AI marketplace on the Solana blockchain. This tool allows users to discover, buy, and sell AI services directly between decentralized agents. Features include comprehensive crypto intelligence, such as sentiment analysis and trending data; DeFi yield tracking; Web3 wallet security analysis (e.g., rug detection); and real-time GPU pricing, making it a comprehensive hub for Web3 AI interactions.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive guide for ML research groups planning multi-venue conference submissions (e.g., COLM). It details the critical path of academic publishing, emphasizing backward induction from hard deadlines (abstract, paper, rebuttal, decision). The workflow covers compute budget allocation, co-author coordination, claim tracking, and managing the technical pipeline from initial topic selection to final presentation, ensuring reproducibility and timely execution in fast-paced AI research cycles.

ccboard is a comprehensive TUI and Web dashboard designed for monitoring and managing Claude Code usage. It provides unified visibility into token consumption, detailed cost tracking, session history, and the real-time status of Model Context Protocol (MCP) servers. Use it to analyze usage patterns, manage configurations, and ensure cost efficiency across all projects.