Skill UI

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Automates cross validation setup for ML training requests, offering best-practice guidance, production-ready code samples, and validation steps across data preparation, model tuning, and experiment tracking.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

FlowIO is a specialized, low-level library designed for reading, inspecting, and writing Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect detailed metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset structures. It is crucial for data preparation, format conversion, and foundational metadata management in bioinformatics pipelines, but does not perform complex analysis like gating or compensation.

This skill provides a comprehensive pre-submission checklist for manuscripts targeting Atmospheric Chemistry and Physics (ACP), an EGU journal. It details the journal's scope—covering aerosols, clouds, chemistry, and dynamics—and outlines the rigorous standards required, including open-access policies, mandatory FAIR data practices, detailed methodological transparency, and preparation for interactive public peer review. Use this guide to assess the fit and completeness of your scientific work.

A comprehensive guide for authors preparing replication and transparency packages for submissions to the British Journal of Political Science (BJPS). It outlines requirements for depositing quantitative and qualitative data and code into the BJPolS Dataverse, ensuring that research findings are fully reproducible and adhering to the Data Access and Research Transparency (DA-RT) statement.

This skill offers a comprehensive project management framework for the end-to-end submission process of data mining research to major conferences like ICDM. It guides users through critical milestones, including topic selection, experimental design, abstract submission, and final camera-ready preparation. It emphasizes backward planning from the final deadline, addressing unique constraints such as no-rebuttal policies, short-paper contingencies, and strict page limits.

A comprehensive guide for structuring and polishing empirical tables and figures for top-tier academic journals, such as the Journal of Business Venturing (JBV). It covers the standards for presenting descriptives, main model coefficients, interaction plots, survival curves, process models, and qualitative data structures. This skill ensures exhibits are self-contained, interpretable, and adhere to rigorous academic house styles.

This guide provides best practices for preparing code, data, and supporting artifacts for academic submissions, focusing on achieving reproducibility for peer review (especially in double-blind settings). It covers crucial steps including artifact anonymization, establishing clear data availability statements, structuring the repository (README, env, data, src), ensuring licensing compliance, and planning for permanent post-acceptance archiving.

This guide helps researchers meet rigorous open science and transparency requirements, such as those mandated by journals like JAP. It details the process of depositing data, analysis code, and materials with persistent identifiers (DOIs), drafting comprehensive data-availability statements, and correctly managing prior-use disclosures and pre-registrations. Essential for ensuring research reproducibility and publication compliance.

A comprehensive checklist for auditing submissions to top-tier data mining conferences like KDD. This guide covers critical areas including track declaration, OpenReview profile completeness for all authors, adherence to strict page limits, double-blind anonymity rules, and quantifying post-launch performance evidence. Essential for maximizing acceptance chances.