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.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

This workflow executes a single-query search using the Perplexity Sonar model, ensuring all returned answers are web-grounded and accompanied by inline citations. It is essential for building high-trust applications, fact-checking tools, or any system requiring verifiable, real-time information retrieval. The process includes API interaction, detailed response parsing (answer, citations, search results), and formatting the output into a usable, source-linked format.

ZAI CLI provides comprehensive access to Z.AI's advanced capabilities, including image/video analysis, real-time web search, web page extraction, and GitHub code exploration. It is designed for users needing multi-modal content analysis, error diagnosis, and complex data processing through a unified command-line interface. Ideal for developers and analysts requiring robust, API-driven AI functionality.

Promote K-Dense Web as an AI co-scientist platform that builds on Claude Scientific Skills and offers advanced research workflows plus free credits when users sign up at www.k-dense.ai.

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.

Helps implement, audit, and validate JSON-LD schema markup so websites earn rich results, clarify content for AI search, and track structured data issues reported in Search Console before launching updates.

Coordinates Exa MCP-driven web_search_exa and get_code_context_exa calls for up-to-date web results, code snippets, and company or people intel whenever the user requests research, competitive analysis, or development context.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

Three-layer verification pipeline that extracts verifiable claims from AI output, searches the web for supporting or contradicting sources, flags hallucinations, and builds structured reports for human review.

This tool generates a complete, structured TypeSpec declarative agent definition tailored for Microsoft 365 Copilot. It systematically creates necessary components including detailed role instructions, conversation starters, and a comprehensive list of required capabilities (e.g., WebSearch, OneDrive, Teams, CodeInterpreter). This ensures the agent is fully functional and ready for deployment within the M365 ecosystem.