Skill UI

AI-powered search using Perplexity models via LiteLLM and a single OpenRouter API key for real-time, web-grounded answers with citations, ideal for up-to-date research, literature surveys, fact checking, and domain-specific queries beyond model cutoffs.

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

Curated catalog of scientific datasets, models, and interactive Spaces on Hugging Face across 17 domains like biology, chemistry, physics, and climate. Helps researchers discover, select, and load scientific ML resources via datasets, transformers, and gradio_client.

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.

This comprehensive guide outlines the principles, architecture, and best practices for developing specialized, modular skills that extend large language model capabilities. Learn how to integrate domain expertise, multi-step workflows, API tool integrations, and structured resources while managing the context window efficiently.

Extract structured domain knowledge from AI models in-session or via Ollama, with quality scoring, search, export, and local model support. No API key needed for closed models.

This skill covers setting up infrastructure for fine-tuning Large Language Models (LLMs) using techniques like QLoRA, LoRA, and full fine-tuning. It includes configuring distributed training with DeepSpeed or FSDP, utilizing frameworks such as Hugging Face TRL and Axolotl, and implementing alignment methods like RLHF and DPO. Suitable for domain-specific model adaptation and production deployment.

Learn to merge multiple fine-tuned models using mergekit without retraining. Covers methods like SLERP, TIES-Merging, and Task Arithmetic to combine domain expertise (math, coding, chat). Improves performance and reduces costs by blending capabilities efficiently. Ideal for creating specialized AI models and rapid experimentation.