Skill UI

This MCP server provides a persistent and searchable memory bank for AI agents. It enables reading, writing, and searching long-term knowledge, architecture patterns, and decisions. The system syncs with project documentation and includes a standalone dashboard for visualization. Ideal for managing agent context and knowledge retention across complex workflows.

Python SDK for Azure AI Content Understanding service. Enables multimodal content extraction from documents, images, audio, and video files. Supports prebuilt analyzers for RAG and custom field extraction for automated workflows. Ideal for developers building AI-driven applications.

Extract text, tables, and structured data from documents using the Azure AI Document Intelligence .NET SDK with prebuilt and custom models.

Extract text, tables, and structured data from documents using Azure AI Document Intelligence REST SDK for TypeScript. Supports prebuilt models for invoices, receipts, ID documents, and custom model training.

Azure AI Document Translation SDK for Python enables batch translation of documents with format preservation. Supports Word, PDF, Excel, PowerPoint, and more. Use for scalable document translation with multi-language targets, glossaries, and status tracking.

Provides TypeScript REST-style clients for Azure AI Translator to translate text and documents, detect languages, transliterate scripts, and manage batch document translation with authentication options.

Clarity Gate is a pre-ingestion verification system designed for Retrieval-Augmented Generation (RAG) workflows. It ensures documents entering knowledge bases are properly qualified by checking for uncertainty markers and distinguishing assumptions from facts. This skill produces Clarity-Gated Documents (CGD) and validates Source of Truth (SOT) files. It helps prevent hallucinations by enforcing epistemic quality standards before data ingestion. Note that it verifies document form rather than factual truth, requiring human-in-the-loop validation for accuracy.

This skill covers the core concepts of context engineering for language models, including system prompts, tool definitions, retrieved documents, message history, and tool outputs. It explains attention mechanisms, context windows, and strategies like progressive disclosure to optimize token usage and agent performance.

This skill estimates the probability that a document's prose was written by AI using linguistic analysis. It identifies specific patterns and phrases indicative of AI generation while abstaining on non-prose content like forms or tables. Suitable for educators, publishers, and HR to triage submissions. It provides confidence scores and reasoning but should not be used as sole proof of misconduct.

Extract structured fields from documents like invoices, payslips, and contracts with page grounding. Each value cites its source page and confidence score. Missing values are reported as not found instead of hallucinated. Supports PDF, PNG, and DOCX files. Ideal for accounting, compliance, and data parsing tasks where accuracy is critical. Uses Stipple API for processing.

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.

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.