Skill UI

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 comprehensive tools for AI engineers and researchers to publish, manage, and link research papers directly on the Hugging Face Hub. It supports creating dedicated paper pages, linking papers to corresponding models and datasets, claiming authorship, and generating professional, markdown-based research articles with LaTeX support. Use it to streamline the entire academic publishing workflow from initial draft to final publication.

Guides building SEO-optimized pages at scale by combining templates, structured data, and proprietary insights, helping teams choose the right playbooks, validate demand, and keep each page unique and valuable while avoiding thin-content penalties.

Performs a full GEO audit of any website by discovering business type, crawling prioritized pages, and delegating parallel subagents to evaluate AI visibility, platform readiness, technical infrastructure, EEAT content, and schema markup, then outputs a composite GEO score with prioritized actions.

Generates and validates llms.txt files, the emerging standard that tells AI systems how to prioritize and understand a website; crawls content, ranks pages, and enforces formatting so your brand narrative stays consistent.

This skill builds a persistent knowledge base combining LLM wiki patterns with structured graphs. It ingests documents once to generate wiki pages and extract entities/relations, avoiding re-derivation. It supports agents with evidence-based querying, wiki search, and knowledge accumulation, ensuring cross-references are never lost.

This skill automatically enriches research wiki pages that are left as bare scaffolds after initial ingestion. It reads multiple external sources (like arXiv/alphaxiv) and fills in 10 structured `_TODO._` sections (e.g., Problem/Gap, Key Results, Open Questions) with detailed prose summaries. Use this skill to transform incomplete research scaffolds into a comprehensive, structured knowledge base.

A comprehensive security skill designed to detect and mitigate indirect prompt injection attacks. It scans untrusted external content—including web pages, PDFs, and images—for hidden, obfuscated instructions before an LLM agent processes them. The pipeline combines content extraction (HTML, OCR), normalization, and dedicated ML models (e.g., LLM Guard, Prompt Guard 2) to ensure data safety.

A technical specialization focused on Generative Engine Optimization (GEO). This skill ensures that specific web pages are structured, documented, and optimized for modern AI answer engines (Google AI Overviews, ChatGPT, Bing Copilot). It focuses on technical elements like structured data (JSON-LD), semantic HTML, and robust metadata to maximize discoverability, trustworthiness, and quotability for AI consumption, rather than traditional SEO ranking.

This tool enables complex web interactions using plain natural language commands. Instead of relying on unreliable selectors, it utilizes a powerful page-agent and LLM loop to interpret user intent, performing actions like filling forms, clicking buttons, and extracting data from dynamic websites. Ideal for testing and automation in pages with ambiguous or changing markup.

A comprehensive pre-submission audit guide for authors submitting papers to EACL (Computational Linguistics). It covers critical areas such as adherence to format gates (8/4 content pages, required Limitations section), strict anonymity requirements, mandatory declarations (Responsible NLP, preprint status), and crucial advice regarding the single, non-fallback submission cycle. Use this before uploading to the ACL Rolling Review cycle.

This guide outlines best practices for structuring advanced NLP/AI research papers for academic submission. It details the critical differences between core content pages, mandatory limitations sections, discretionary appendices, and supplementary uploads, ensuring that reviewers are properly guided and that the work remains reproducible and verifiable.