Skill UI

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.

scVelo is a powerful Python package designed for RNA velocity analysis using single-cell RNA-seq data. It estimates cell state transitions by modeling the kinetics of mRNA splicing—specifically using the ratio of unspliced to spliced mRNA abundances. This allows researchers to reconstruct developmental trajectories and predict cell fates, even when only snapshot data is available, making it a vital complement to standard trajectory inference tools like Scanpy.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

Comprehensive toolkit for managing sensitive Flexport supply chain data, including shipment records, customs declarations, and financial invoices. This skill enforces strict data governance, handling PII redaction, ensuring GDPR/CCPA compliance, managing mandated data retention periods (e.g., 5-7 years), and blocking restricted export items (ITAR) to maintain global trade compliance standards.

A structured guide for setting up a robust local development environment for Palantir Foundry data pipelines. This workflow enables users to run Python transforms and unit tests using PySpark against sample data, and mock the Foundry API client, ensuring fast, isolated iteration cycles before deploying code to the official Foundry environment. Ideal for data engineers and developers building complex ETL logic.

This skill integrates ten major academic databases (including PubMed, arXiv, OpenAlex, and Semantic Scholar) via REST APIs. It enables comprehensive searches for scholarly articles, preprints, citations, and full-text access across various fields. Use this when searching for research papers on a topic, looking up specific DOIs/PMIDs, or needing global literature metadata.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

Comprehensive guidelines covering best practices for Power BI report development. This includes establishing a consistent design identity, applying visual hierarchy rules (like the 3-30-300 rule), managing color discipline, ensuring accessibility, and structuring pages based on their intended question or purpose. It helps transform raw data into professional, actionable, and user-friendly reports.

Provides a structured, actionable review of Power BI reports across their entire lifecycle—from development to active use. The tool assesses report effectiveness, usage adoption, data performance (load times), and structural integrity. It generates a prioritized list of findings and concrete recommendations, helping users determine if reports are worth maintaining, optimizing, or retiring.