Skill UI

This skill provides expertise in designing the critical interface between Large Language Models (LLMs) and external tools. It focuses on tool schema design, function calling best practices, and robust error handling to prevent common failures like hallucination or silent loops. It ensures that the tool's documentation and schema are crystal clear, allowing the LLM to use complex external APIs reliably and predictably.

Azure AI Content Understanding SDK for Python orchestrates multimodal extraction from documents, images, audio, and video to feed RAG pipelines or automate structured workflows.

Azure AI Content Understanding SDK for Python orchestrates multimodal extraction from documents, images, audio, and video to feed RAG pipelines or automate structured workflows.

TypeScript REST SDK for Azure Document Intelligence that extracts text, tables, invoices, receipts, and structured data from documents via prebuilt or custom models.

TypeScript REST SDK for Azure Document Intelligence that extracts text, tables, invoices, receipts, and structured data from documents via prebuilt or custom models.

Provides Azure REST-style TypeScript clients for text and document translation with language detection, transliteration, and batch processing for localization workflows.

Clarity Gate ensures documents entering RAG knowledge bases retain epistemic quality by generating Clarity-Gated Documents, validating Source of Truth tables, and running deterministic claim/document hash scripts before ingestion.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

Guides how to stretch limited LLM context windows by compressing, masking, caching, and partitioning information so agents process longer conversations or documents without enlarging models; includes when to trigger optimizations and concrete patterns for summaries, masking, KV-cache reuse, and budget tracking.

Cursor AI Debug Toolkit helps identify root causes of incorrect Cursor AI suggestions, inspect completion/chat context, and use dev tools/logs to tweak settings, re-test models, and document fixes for future issues.

Implements Gamma’s core workflow for generating presentations via AI from prompts, documents, outlines, or batches, handling styles, images, notes, and error recovery to automate content-to-slides conversion.

Guides developers through using Google’s Gemini API to access advanced multimodal models, covering SDK installation, sample code in Python/JS/Go, REST specs, and documentation indexes for text, image, and function-calling workflows.