Skill UI

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

A comprehensive workflow utilizing the Perplexity Sonar API for advanced, multi-turn research. It systematically decomposes broad topics into focused sub-queries, executes batch queries while maintaining context continuity, deduplicates citations, and synthesizes all findings into a structured, comprehensive research document. Ideal for academic research, market analysis, and generating detailed investment or industry briefs.

Guides a Power BI DAX expert through performance, readability, best-practice, and maintainability reviews; recommends variable reuse, context tuning, and error handling; then outputs an optimized formula plus justification for analytics models.

This advanced technique allows users to identify an obscure actor by linking indirect clues across three domains: the director, the film's characteristics, and the actor's biographical details. It is essential when direct searches fail, using the director's work or a film's context as the primary entry point to resolve the actor's identity.

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

This tool performs rigorous, zero-context verification of academic papers. It compares every quantitative claim (numbers, percentages, scope) found in the paper text against the original, raw result files. By acting as a fresh reviewer with no prior context, it is designed to prevent confirmation bias and detect critical issues such as data rounding errors, cherry-picking best seeds, or mismatched experiment configurations, ensuring maximum paper-to-evidence fidelity before submission.