Skill UI

A cross-protocol platform for discovering and searching 18,000+ MCP servers and AI agents across six or more registries. It allows users to find the necessary endpoint for specific tasks (like database access or file conversion) by searching across MCP, A2A, and agents.txt protocols simultaneously.

This advanced workflow is designed to generate comprehensive, fact-checked research reports on any complex topic. It systematically performs fan-out web searches across multiple defined angles, fetches multiple sources, and employs a rigorous adversarial verification system (3-vote) to validate claims. The final output is a highly cited, synthesized report, ideal for academic or critical business research.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

Delegate bounded coding tasks to the Grok Build CLI while the orchestrator keeps review and commit ownership. Covers writing briefs, dispatching runs, polling results, re-verifying gates, and landing changes.

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.

Estimates VRAM/memory required for inference of Safetensors or GGUF models from Hugging Face Hub, using HTTP range requests without downloading weights. Supports KV cache estimation with experimental flag.

Reduces token costs by scoping context into directory-level CLAUDE.md files. Sets up hierarchical memory, tracks savings via dashboard, routes agents to sub-contexts, and provides audit and insights commands.

Build background coding agents in remote sandboxed environments with image registries, warm pools, snapshots, and self-spawning sessions for scalable multiplayer execution.

Build container-based hosted agents using Azure AI Projects SDK and ImageBasedHostedAgentDefinition. Covers installation, authentication, agent versioning, resource allocation, protocol versions, and tool configuration in Azure AI Foundry.

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.

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Handles backend selection, local GPU evals, and choosing between vLLM, Transformers, and accelerate.

This skill enables users to add and manage structured evaluation results within Hugging Face model cards. It supports extracting evaluation tables from README files, importing benchmark scores via the Artificial Analysis API, and executing custom model evaluations using vLLM or lighteval backends. It ensures compatibility with the model-index metadata format for standardized model releases.