Skill UI

A comprehensive Python SDK for accessing Azure AI Language service capabilities. It enables advanced Natural Language Processing (NLP) features, including sentiment analysis, named entity recognition (NER), key phrase extraction, language detection, and sensitive PII detection. Ideal for building sophisticated text analysis applications and data pipelines.

This SDK provides Java developers with access to advanced image analysis features from Azure AI Vision. It allows applications to perform various visual tasks, including OCR text extraction, generating detailed image captions, detecting specific objects with bounding boxes, recognizing content tags, and performing smart cropping. Use it when building applications that require deep visual understanding of images.

This SDK provides a comprehensive Python client for Azure AI Vision 4.0, enabling advanced image understanding tasks. It supports generating descriptive captions, extracting content tags, detecting objects and people with bounding boxes, performing OCR (text recognition), and suggesting smart crop regions. It is ideal for computer vision applications and detailed image content analysis.

This skill serves as a comprehensive health assistant, designed to analyze complex medical information, track symptoms over time, and provide personalized wellness guidance. It is useful for users seeking structured health monitoring and preliminary insights into their condition, but it must not replace professional medical diagnosis or advice.

Uses the Gemini API to autonomously plan, search, read, and synthesize detailed research reports with streaming status updates, structured outputs, and the ability to continue previous investigations for market analysis, technical reviews, or due diligence.

This skill provides capabilities for bidirectional audio processing using fal.ai models. It supports Text-to-Speech (TTS), converting written text into natural-sounding speech, and Speech-to-Text (STT), accurately transcribing spoken audio into readable text. Use this skill for applications requiring high-quality audio input analysis or synthetic voice generation, such as virtual assistants, content digitization, or multilingual communication tools.

This workflow connects to the Fireflies.ai API via GraphQL to fetch and process detailed meeting transcripts. It handles speaker-diarized data, extracting structured insights such as action items, key topics, sentiment analysis, and speaker breakdowns. It enables the creation of comprehensive meeting intelligence reports and robust downstream pipelines. Use this when you need to automate the retrieval, summarization, or deep analysis of recorded meeting content.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

A comprehensive workflow solution for Groq, enabling powerful multimodal tasks beyond simple chat completions. It supports ultra-fast audio transcription (Whisper), detailed image understanding using vision models, and professional text-to-speech generation. Use this for real-time content processing, multimedia analysis, and automated content creation.

Enables Claude to invoke the computer-vision-processor plugin, auto-generating Python calls to run object detection, classification, and segmentation on user-provided images for detailed analysis.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

A comprehensive, model-agnostic toolkit for advanced LLM development. It enables iterative prompt optimization through static analysis (token usage, clarity), measures critical RAG metrics (relevance, faithfulness, coverage), and validates complex agent workflows and configurations. Essential for rigorous LLM quality assurance and cost management.