Skill UI

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.

A comprehensive guide to designing high-performance schemas in ClickHouse. Learn how to select the optimal MergeTree engine (e.g., ReplacingMergeTree, AggregatingMergeTree), structure the ORDER BY key for maximum query efficiency, and implement effective partitioning strategies for large-scale analytical workloads and ETL processes.

This skill guides the setup of CI/CD pipelines for Hex data analytics projects using GitHub Actions. It enables running automated unit tests on pull requests and triggering live project runs on the main branch merge. Use this when establishing automated testing workflows, validating data transformation logic, or integrating Hex outputs into a continuous delivery process.

This tool processes raw search results and bibliography entries to generate a highly structured and curated source pack for long-form academic papers. It categorizes sources into primary, supporting, and weak/excluded sources, ensuring the resulting literature review is reliable, diverse, and methodologically sound. Essential for academic writing workflows.

This skill provides a comprehensive, step-by-step workflow for building complete Power BI reports from scratch using the pbir CLI. It guides users through model discovery, defining design briefs, visual layout, data validation, and final publishing. Ideal for automating the entire business intelligence (BI) reporting process and ensuring adherence to professional design standards.

This tool analyzes and maps the downstream dependencies of a semantic model across multiple Fabric workspaces. It is crucial for performing impact assessments before making modifications, auditing connected reports, or mapping cross-workspace data flows. It automates the lineage discovery process, providing comprehensive visibility into which reports rely on a specific data source.

This guide details the process of creating advanced, static visualizations in Power BI using Python libraries like matplotlib and seaborn. It covers the full workflow, from script writing and data injection to best practices and limitations, enabling users to render complex, custom charts not supported by native Power BI visuals.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

This guide provides a comprehensive framework for understanding the academic peer review process used at AISTATS, covering everything from OpenReview submission workflows and author-reviewer dynamics to advanced decision criteria (correctness, significance, empirical support). It helps researchers strategize their submissions and responses to maximize acceptance chances in statistics and machine learning.

This comprehensive router guides authors through the entire academic submission process for top-tier journals. It maps out the necessary steps—from initial topic selection and literature review to advanced econometric techniques (DID, mechanism, robustness) and handling reviewer rebuttal—ensuring the manuscript meets the rigorous standards of empirical research.

This comprehensive workflow guides authors through the entire submission process for The Econometrics Journal (EctJ). It provides staged checkpoints—from initial topic selection and theoretical framing to data analysis, manuscript compression, and rebuttal. It ensures manuscripts are methodologically rigorous, empirically grounded, and adhere to strict publishing standards required by top academic venues.