Skill UI

This skill provides comprehensive operations for managing NodeGroup resources. It allows users to query, create, update, delete, and manage bindings for nodes, namespaces, and workspaces within the edgewize nodegroup environment. Ideal for infrastructure automation, troubleshooting, and API-driven cluster management.

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.

This skill serves as a compatibility demonstration to verify that the Hermes Agent can successfully parse and load an SKILL.md file generated from an MDA source. It confirms that the compiled structure, including specific frontmatter and reserved metadata namespaces, survives the round-trip compilation process, ensuring structural integrity for the agent's skill registry.

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.

This skill provides comprehensive auditing of Fabric and Power BI tenant settings against a curated governance baseline. It detects configuration 'drift,' enumerates delegated overrides across capacity, domains, and workspaces, and investigates associated Entra security groups. It translates complex API findings into grounded, actionable discussions, helping users understand functional consequences and risks.

This skill provides comprehensive, expert guidance for utilizing the Fabric Command Line Interface (CLI) using the `fab` tool. It enables programmatic interaction with the entire Fabric and Power BI service cloud environment, including workspaces, semantic models, data assets, and configurations. Use it to automate tasks like authentication, metadata retrieval, data querying (DAX, T-SQL, DuckDB), and resource management, ensuring best practices and error handling are maintained.

This tool analyzes and maps the downstream dependencies of a semantic model across multiple Fabric workspaces. It is crucial for performing impact assessments before making modifications, auditing connected reports, or mapping cross-workspace data flows. It automates the lineage discovery process, providing comprehensive visibility into which reports rely on a specific data source.

A spectral vector search tool that enhances nearest-neighbor search with graph Laplacian features, capturing both semantic similarity and structural roles in embedding spaces.

This skill provides comprehensive access to the Hugging Face Hub ecosystem via MCP server tools. Users can search for state-of-the-art models, datasets, and research papers, retrieve detailed repository information, fetch documentation, run compute jobs on GPU/CPU, and interact with Gradio Spaces as fully functional AI tools. Ideal for ML development, research, and application prototyping.

Automates the creation and publication of polished, functional Gradio demos on Hugging Face Spaces for any user-provided LoRA model. This skill handles the complex workflow of designing the optimal UI, selecting the correct inference pipeline (e.g., text-to-image, video-to-video), and generating all necessary files (app.py, requirements.txt, README.md) to deploy a complete, ready-to-use AI playground.

This skill guides users through building, deploying, and maintaining machine learning applications on Hugging Face Spaces. It provides comprehensive knowledge on selecting the optimal SDK (Gradio, Docker, Static), choosing appropriate hardware resources (ZeroGPU, dedicated GPUs, CPU-basic), and following best practices to ensure ML applications are deployed efficiently and successfully on the platform.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.