Skill UI

A comprehensive guide for selecting and optimizing embedding models for advanced vector search and retrieval-augmented generation (RAG) applications. This guide covers model comparison, chunking strategies, dimension reduction techniques, and provides runnable Python templates for both OpenAI and local Sentence Transformer implementations.

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

This advanced workflow extends beyond basic search capabilities. It allows users to discover pages semantically similar to a given URL, retrieve detailed content, highlights, and summaries from known URLs (batch processing), and generate comprehensive, cited answers using AI. Ideal for deep academic research, competitive intelligence, and content enrichment pipelines.

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 skill enables the generation of high-quality images and videos using advanced fal.ai AI models. It is designed for creative professionals, artists, and developers who require powerful, generative visual content. Use this when your task involves transforming text prompts into sophisticated visual media, making it ideal for concept art, multimedia prototyping, or advanced content creation.

Automates Flask-based machine learning API deployment by guiding you through best practices, generating production code/configurations, and validating outputs for serving, monitoring, and MLOps pipelines.

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.

This comprehensive guide provides documentation and code examples for utilizing the Gemini API, Google's advanced multimodal AI platform. It enables developers to power applications with cutting-edge capabilities, including text generation, multimodal understanding (images, audio, video), function calling, and structured data output. Supports major languages like Python, JavaScript, and Go.

Automates GPU resource optimization for ML deployments by guiding best practices, generating production-ready configurations, and validating setups for model serving, MLOps pipelines, and monitoring.

Gradient Clipping Helper automates ML training work by guiding gradient clipping workflows, generating production-ready code and configs, and validating outputs for PyTorch, TensorFlow, and related pipelines.

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.

Automate AI video creation workflows via the HeyGen-Composio MCP integration to browse avatars/templates, populate variables, generate videos, check processing status, and fetch shareable URLs.