Skill UI

ADHX provides a dedicated API to fetch X/Twitter posts as clean, structured JSON. It extracts comprehensive data, including author profiles, engagement metrics (likes, retweets), and full article content for long-form posts. This method is vastly superior to traditional scraping techniques, making it ideal for LLM analysis, summarization, and structured data workflows.

A comprehensive tool for extracting YouTube transcripts, searching videos, browsing channels, and monitoring new uploads. It supports detailed transcript analysis, content summarization, and playlist management by leveraging the TranscriptAPI. Ideal for researchers, content strategists, and market analysts who need structured data from video content without downloading files.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.

This framework provides a rigorous methodology for conducting systematic literature reviews and comprehensive academic syntheses. It guides researchers through building transparent coverage, designing robust coding matrices, and choosing the correct integration method (narrative, systematic, or bibliometric) to move beyond simple summarization and create an auditable intellectual map of a field.

This skill automatically converts structured data from Excel (.xlsx) workbooks into a clean, readable Markdown format. It is essential for enabling accurate analysis, summarization, and data extraction of spreadsheet contents, especially when the user asks to 'read', 'analyze', or 'compare' data without specifying a conversion method. It supports both single files and batch processing of entire folders.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

This guide details the rigorous methodology for conducting systematic literature reviews and meta-science pieces, particularly for integrative reviews in psychology (PoPS style). It instructs researchers on how to systematically gather evidence across multiple sub-areas by utilizing techniques like forward/backward snowballing, comprehensive database sweeps (e.g., PsycINFO), and developing detailed evidence matrices. The focus is on synthesizing findings and establishing replicable search protocols to ensure academic rigor and comprehensiveness, moving beyond simple summarization.