Skill UI

A specialized tool for safely reading, validating, and exporting protocols.io data using current, official REST/MCP contracts. It provides a read-only, planned workflow to ensure data integrity, supporting complex scientific data extraction and workflow automation while strictly adhering to API governance and security best practices.

This guide provides comprehensive, version-aware documentation for using PufferLib in reinforcement learning pipelines. It details best practices for adapting Gymnasium/PettingZoo environments, managing different PufferLib versions (3.0.0 vs 4.0 source), and executing safe, reproducible training and evaluation routines. Key focus areas include environment contract validation, sandboxing native builds, and maintaining data provenance.

pydicom is a comprehensive Python library designed for reading, inspecting, writing, and transforming local DICOM datasets and associated pixel data. It supports complex metadata handling, various transfer syntaxes, and provides tools for bounded de-identification and preflight checks. It is essential for medical image processing pipelines that require robust handling of Protected Health Information (PHI).

PyLabRobot provides a comprehensive, software-only framework for developing and reviewing complex laboratory automation protocols, including liquid handling and resource management. It simulates offline device interactions and resource tracking, ensuring rigorous planning without physical hardware connection. It mandates explicit human verification before any live execution, prioritizing safety and reproducibility.

A powerful Python library for analyzing, validating, converting, and transforming complex materials structures and associated computational data. It provides functionalities for handling compositions, periodic crystals, molecules, local phase diagrams, symmetry assignments, and electronic structure I/O, ensuring strict provenance tracking for all transformations.

Pymoo is a comprehensive Python library designed for tackling complex optimization problems, especially those involving multiple conflicting objectives. It implements state-of-the-art evolutionary algorithms (like NSGA-II, MOEA/D) to efficiently locate the Pareto front. It is invaluable for engineering design, scientific modeling, and decision-making processes requiring the analysis of trade-off solutions.

PySam provides a powerful Python interface for low-level, streaming access to HTSlib-supported genomic data formats. It enables reading, querying, filtering, and writing complex formats such as SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix-indexed tables. It is essential for bioinformatics pipelines requiring precise control over genomic data manipulation, including pileup and coverage calculations.

PyTDC is a specialized Python package designed to interact with the Therapeutics Data Commons. It facilitates the discovery of machine learning tasks, loading approved datasets, generating task-appropriate data splits, and evaluating predictions for drug discovery research. The tool strongly emphasizes performing metadata-only discovery and planning before initiating resource-intensive data downloads or network operations.

PyTorch Lightning is an advanced deep learning framework built on PyTorch. It simplifies the entire ML workflow, allowing users to organize models into LightningModules, automate training loops using the Trainer, and manage complex data pipelines with DataModules. It supports advanced features like multi-GPU/TPU scaling (DDP, FSDP), integrated logging (W&B, MLflow), and custom callbacks, making large-scale neural network development efficient and reproducible.

Pyzotero is a comprehensive Python wrapper designed to interact with the Zotero Web API v3. It allows users to programmatically manage bibliographic references, including retrieving, creating, updating, and deleting items, collections, tags, and attachments. Ideal for building research automation workflows, exporting citations, and integrating Zotero data into larger applications.

A comprehensive guide for diagnosing production performance issues in Qdrant vector databases. Learn how to systematically check the optimizer status, analyze high memory usage (OOM crashes), and troubleshoot slow query latencies using key metrics and dedicated API endpoints. Essential for maintaining optimal operational health.

Qiskit is a comprehensive SDK for developing quantum algorithms. It enables users to build, optimize, simulate, and execute quantum circuits using modern APIs (Qiskit 2.x). It supports local exact sampling, high-performance noisy simulation via Qiskit Aer, and direct execution on IBM Quantum hardware through the Runtime service, making it a powerful tool for quantum research and development.