Skill UI

Utilize this tool to programmatically access and parse search engine result pages (SERP) data from multiple sources, including Google, Bing, YouTube, and Google News. It returns comprehensive, structured JSON data, allowing developers and marketers to extract organic results, knowledge graph entries, ads, and local information for advanced SEO analysis, market research, and data science projects. Supports both Python and Node.js implementations.

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.

This guide provides advanced search strategies for tracking Asian music, including K-pop, J-pop, and Filipino genres. It details how to construct highly specific queries by incorporating regional keywords, understanding idol comeback cycles, and utilizing cultural details (such as zodiac or national identity) to narrow down search results and avoid generic noise.

This technique guides users on how to use visual descriptions from music videos (MV), such as specific scenes, emotional themes (grief, loss), or real-world events, as powerful search anchors. Instead of only searching by title or artist, users can extract keywords from the visual narrative to refine search queries, significantly increasing the chances of identifying the correct song or tracing its origin. Ideal for music research and cultural analysis.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.

This tool functions as a post-task epilogue, designed to audit and record the entire research journey. It scans the conversation history at the end of a coding or research session to extract critical research artifacts, including decisions, experiments, dead ends, claims, heuristics, and major pivots. It systematically writes this provenance data into the structured `ara/` directory, ensuring a faithful and auditable trace of how the project truly evolved.

This tool performs a rigorous Level 2 semantic epistemic review of Agent-Native Research Artifacts (ARAs). It assesses six critical dimensions—including evidence relevance, falsifiability, and methodological rigor—to determine the content's intellectual soundness. It is used after initial structural validation to provide objective, constructive criticism, scoring the work from Strong Accept to Reject.

This skill leverages the Gemini AI model to perform comprehensive and broad literature discovery across academic domains. Unlike traditional APIs, it decomposes complex research queries into sub-problems, explores topic aliases, and surfaces highly relevant papers that might otherwise be missed. It is ideal for conducting deep, multi-angled literature reviews and achieving a holistic view of a research field.

Hugging Science is a curated, LLM-friendly index specializing in AI/ML applications across diverse scientific domains, including biology, chemistry, physics, and medicine. It helps researchers discover pre-filtered datasets, state-of-the-art models, and interactive demos for solving complex scientific problems, ensuring high signal quality beyond generic search.

A powerful skill for searching and retrieving comprehensive academic metadata using the OpenAlex API. It provides a unified view of research papers, including full open citation graphs, detailed institutional affiliations, and crucial funding information (e.g., NSF, NIH). Ideal for deep academic research, comparative literature reviews, and identifying research trends across multiple disciplines.

This skill analyzes the user's historical workflow data, passively captured via a local screenpipe daemon. It detects recurring activity patterns (e.g., research steps) and clusters them. It then compares these patterns against existing skills, automatically drafting proposals for new skills or optimized composition recipes that chain existing tools together. It is designed for privacy, running all detection and processing locally, and only sending redacted summaries to the LLM.