Skill UI

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

A state-of-the-art memory search engine providing multi-faceted retrieval strategies. It supports default dense semantic matching, advanced hybrid search (sparse + dense with RRF), deep multi-hop Graph RAG for complex reasoning, and 'Smart' search utilizing query expansion, recency boosting, and MMR diversity reranking. Ideal for retrieving nuanced, diverse, and time-sensitive information from large, complex knowledge bases across multiple namespaces.

Manage Azure Machine Learning resources including workspaces, data assets, models, compute clusters, jobs, pipelines, and environments using the Azure ML SDK v2 for Python. Ideal for end-to-end ML workflow automation.

This skill enables AI assistants to interact with the Hugging Face Hub via Model Context Protocol (MCP). Users can search for models, datasets, and papers, retrieve repository details, fetch documentation, and execute compute jobs on GPU or CPU. It also allows leveraging Gradio Spaces as functional AI tools. Ideal for developers and researchers needing direct access to the HF ecosystem for model discovery, training, and deployment workflows.

The Hugging Face Hub CLI (hf) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more. Supports authentication, cache management, and bucket operations.

Look up Hugging Face paper pages as markdown and query the papers API for structured metadata including authors, linked models, datasets, Spaces, GitHub repos, and project pages. Ideal for summarizing or analyzing AI research papers from hf.co or arXiv URLs and IDs.

Trackio is a library for logging and visualizing machine learning training metrics. It supports Python API for logging metrics and firing alerts during training, and CLI for retrieving data. Metrics sync to Hugging Face Spaces for real-time dashboards. Ideal for autonomous ML workflows and experiment management.

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. The skill generates a custom interactive app with the right controls, loading, and inference recipe, then publishes it as a private Space.

Build, deploy, and maintain machine-learning applications on Hugging Face Spaces using Gradio, Docker, or Static SDKs, with ZeroGPU and dedicated hardware, model loading, debugging, and inference providers.

Rules and patterns for building AI demos on Hugging Face Spaces with ZeroGPU hardware, covering @spaces.GPU, quota tuning, CUDA constraints, and concurrency safety.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.