Skill UI

This skill provides comprehensive guidance on leveraging d3.js (Data-Driven Documents) to create sophisticated, interactive, and publication-quality data visualizations. It covers core concepts like data binding, implementing various chart types (network graphs, geo-maps, etc.), handling complex transitions, and ensuring responsiveness across modern JavaScript frameworks (React, Vue, Vanilla JS). Learn the standard workflow for scale definition, axis creation, and drawing custom visual elements with fine-grained control.

A comprehensive guide for integrating Plaid API into fintech applications. Covers secure link token creation, public token exchange for persistent access tokens, and real-time transaction synchronization using the /transactions/sync endpoint. Includes best practices for identity verification, ACH authentication, and webhook handling, ensuring robust financial data connectivity.

Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.

Provides a Python client for querying the Data Commons knowledge graph so teams can resolve entities, fetch statistical observations, and explore public demographic, economic, and health datasets with optional Pandas support.

Query and download the NCI Imaging Data Commons public cancer imaging datasets via idc-index, filter by metadata, fetch DICOM files, visualize series, and respect licensing details for AI training or research.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

Direct REST/API access to PubMed for constructing complex Boolean or MeSH queries, filtering by publication attributes, and automating literature retrieval or citation management via NCBI’s E-utilities.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

This tool provides comprehensive brand reputation monitoring by scraping reviews, ratings, and mentions from a vast array of platforms, including Google Maps, Booking.com, TripAdvisor, Facebook, Instagram, YouTube, and TikTok. It utilizes Apify Actors to gather structured sentiment data, allowing users to track public perception, identify key trends, and summarize overall brand health over time. Ideal for market research, competitive analysis, and customer experience tracking.

Build an automated AI-driven collector that monitors any public source—jobs, pricing, news, GitHub, sports, etc.—enriches results via Gemini Flash, stores them in Notion/Sheets/Supabase, runs on GitHub Actions, and learns from feedback.