Skill UI

This skill provides comprehensive analysis and structured data extraction for academic research papers hosted on Hugging Face and arXiv. It can read paper content in markdown format, retrieve key metadata (authors, abstracts, GitHub repos), and identify linked assets such as associated models, datasets, and Spaces. Use this when the user provides a Hugging Face or arXiv URL, or explicitly requests a summary or explanation of an AI research topic.

This tool automates the end-to-end workflow for GDPR Data Subject Access Requests (DSAR). It handles identity verification, discovers PII across structured and unstructured data sources using NER and regex, maps data to Article 15 requirements, and generates auditable compliance reports, ensuring adherence to ICO/EDPB guidelines and scaling compliance efforts.

This sophisticated tool generates publication-quality AI illustrations and diagrams for academic papers. It implements a multi-stage, iterative workflow, using advanced LLMs (like Claude and Gemini) to parse user requests, optimize complex layouts, verify academic styling (e.g., CVPR/NeurIPS standards), and render high-fidelity figures. Ideal for researchers needing professional, complex visuals for their manuscripts.

Generates structured peer-review quality critiques of academic papers, covering summaries, methodology scrutiny, contribution evaluation, literature positioning, and recommendations across domains whenever a user requests reviews, analyses, or summaries of research publications.

Enforces full, unabridged code and document generation by overriding LLM truncation safeguards, forbidding placeholder patterns, and managing token-limit splits so every requested deliverable is returned complete.

Connects to Google NotebookLM to query your uploaded documentation via Gemini’s source-grounded answers, automating authentication, notebook management, and question workflows through the provided run.py wrapper.

Roundup gathers GitHub, calendar, email, and chat data from your configured sources and synthesizes it into status briefings that match your tone, audience, and requested timeframe.

This technique guides users on how to construct highly effective search queries for movies. When a movie question contains both general plot descriptions and highly specific details (such as exact quotes, precise rating language, or specific award names), the user must prioritize the most discriminative clue. By focusing on unique identifiers, this method significantly improves the accuracy and efficiency of information retrieval.

This skill is designed to facilitate communication and collaboration between different AI agents. Use it when you need to consult an expert agent, request a plan, seek a review, or continue a context-aware conversation with a specialized agent. It handles both real-time foreground chat and asynchronous background task submission, ensuring robust multi-agent workflows.

This skill guides users in analyzing complex questions related to criminal or legal incidents (e.g., murder, theft, arson, accident). By first classifying the specific case type and identifying domain-specific keywords, it dramatically improves the precision of subsequent search queries. It teaches users how to combine case type terms with distinctive constraints like amounts, dates, and locations for maximum search effectiveness.

This skill is designed to translate vague, colloquial, or descriptive biological details (such as behaviors, appearances, or cultural uses) into precise scientific or common species names. It is essential for constructing effective search queries in biological databases when the question does not use formal taxonomic names. By accurately identifying candidate species names from descriptive clues, users can drastically improve search relevance and accuracy.

This technique guides users on how to solve highly complex questions that require tracking a chain of related entities (e.g., Author -> Article -> Mentioned Person). Instead of attempting a single search, the process must be broken down into sequential steps, using the result of one search as an anchor point for the next, thereby building a comprehensive knowledge chain.