Skill UI

Generates a complete, self-contained HTML dashboard for analytics, admin, or control panel views. It structures the layout with a fixed sidebar, top navigation, KPI cards, and detailed charts (using inline SVG). Ideal when the brief requires visualizing key metrics, performance over time, or operational statistics.

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.

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 guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

A comprehensive framework for structuring user interviews, customer discovery, or Jobs-to-be-Done (JTBD) research. This guide provides structured steps for screener creation, deep behavioral exploration (focusing on past actions, not future predictions), and systematic synthesis/pattern detection, ensuring the gathering of genuine, actionable insights.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

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 guides authors through evaluating the suitability of their clinical research manuscript for Annals of Internal Medicine. It provides deep insights into the journal's high standards, including specific methodological requirements (CONSORT, PRISMA, STROBE, STARD, TRIPOD), required evidence synthesis depth, ideal scope (RCTs, systematic reviews, guidelines), and current house style guidelines necessary for successful submission.

This guide outlines a systematic methodology for conducting comprehensive literature reviews in economics. It moves beyond simple summarizing by teaching researchers how to build an evidence matrix, ensuring systematic coverage by sweeping databases (JEL codes, keywords, working papers). It is essential for preparing high-impact academic reviews that must prove exhaustive coverage and synthesize complex, often conflicting, findings.

This skill audits academic literature reviews (like Annual Reviews of Psychology) to ensure they maintain scholarly rigor, balance, and objectivity. It checks for uneven coverage across different labs or theories, ensures evidence is weighted by design strength and replication status (not mere vote-counting), and flags potential self-promotion or bias. Use this when scrutinizing a high-stakes scientific review for academic fairness.

Provides comprehensive guidelines for authors conducting systematic reviews and meta-analyses in psychology. It emphasizes documenting the literature search protocol, ensuring quantitative synthesis rigor (PRISMA flow, effect-size data), and making all findings open and reproducible for high-impact academic publication.

A methodological guide for documenting the rigor of literature reviews, especially in sociology. It details how to build a comprehensive 'coverage account' (a near-PRISMA record of the search process) and how to ensure any quantitative synthesis (meta-analysis, bibliometrics) conducted within the review is fully reproducible by depositing data and code.