Skill UI

A comprehensive skill for analyzing Non-Fungible Tokens (NFTs). It fetches collection metadata from APIs (like OpenSea), parses trait attributes, and calculates advanced rarity scores using multiple algorithms (e.g., rarity_score, information entropy). Use this tool to rank tokens by uniqueness, compare specific NFTs, or analyze the distribution of traits within an entire collection, outputting results in JSON or CSV format. Ideal for crypto research and market due diligence.

This skill manages the complete lifecycle of Fireflies.ai meeting transcripts. It allows users to fetch raw data, export in various formats (JSON, CSV, SRT), sanitize sensitive personal information (PII Redaction), and enforce data retention policies to ensure compliance with regulations like GDPR and CCPA. Ideal for data governance and compliance workflows.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

A comprehensive foundation skill for monitoring cryptocurrency markets. It provides real-time quotes, 24-hour change metrics, and deep historical price data for over 10,000 coins. Users can track specific symbols, view predefined watchlists (e.g., DeFi, Top 10), or export detailed OHLCV data in CSV format. This skill serves as the foundational data layer for crypto portfolio management, tax calculation, and DeFi analysis applications.

Access the ClinicalTrials.gov API v2 to search trials by condition, drug, location, sponsor or status, fetch details via NCT ID, and export JSON/CSV data for research analysis, patient matching, and trial monitoring.

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

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.

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

This skill provides comprehensive read-only access to explore, query, and analyze datasets hosted on Hugging Face. It utilizes the Dataset Viewer API to list splits, preview rows, perform text searches, filter records, and retrieve crucial metadata and Parquet links. Furthermore, it supports advanced data extraction by enabling complex SQL querying (via parquetlens) directly against dataset shards, allowing export into CSV, JSON, or Parquet formats.

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

Aggregates official auctioneer information from all 27 Brazilian Commercial Boards into a SQLite store, exposes a FastAPI REST interface, and enables CSV/JSON exports plus customizable scraping per state.

This tool is designed to analyze complex Machine Learning experiment results from various sources (JSON/CSV). It computes detailed statistics, builds comprehensive comparison tables across hyperparameter variations, and generates structured, actionable insights. It helps researchers interpret findings, identify trends, and propose next steps for research.