Skill UI

A comprehensive skill for managing the entire lifecycle of datasets on the Hugging Face Hub. It allows users to initialize, configure, and stream updates for datasets of various types (Chat, Text Classification, Tabular). Crucially, it extends data handling capabilities by enabling powerful SQL-based querying and transformation directly on Hub datasets using the DuckDB engine, facilitating advanced data exploration and pipeline creation.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

This skill guides users through the complete lifecycle of data quality incident management using Monte Carlo MCP tools. It performs detailed investigation, including root cause analysis and blast radius assessment. It then coordinates various external tools (APIs, CLI) to propose and execute appropriate remediation actions, ensuring all data incidents are resolved with full context.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This comprehensive workflow guides researchers through the entire academic publication lifecycle for submissions to the Chinese Journal of Management Science. It acts as a critical router, ensuring manuscripts cover essential stages: topic selection, advanced model formulation, algorithm design, empirical validation (out-of-sample testing), and generating actionable managerial insights. It helps prevent common pitfalls like lacking empirical depth or misaligning with the journal's core focus on applied modeling.

A comprehensive project management guide for preparing and submitting advanced NLP conference papers to EMNLP. It details the entire research lifecycle, including backward planning from ARR deadlines, managing data annotation timelines, coordinating team roles, and making strategic decisions (commit, revise, withdraw) to ensure successful submission.

A comprehensive orchestrator skill guiding the entire lifecycle of an applied econometrics manuscript targeting the Journal of Applied Econometrics (JAE). It ensures the paper maintains an applied, real-data focus and rigorously follows the necessary steps, including literature positioning, identification strategy, reproducible data analysis, and mandatory data archival deposit. Designed to prevent common pitfalls by integrating data integrity checks at every stage.

A comprehensive project management skill designed for academic submissions in Recommender Systems (RecSys). It guides users through the entire research lifecycle, from initial venue fit and data freezing to abstract submission, full paper writing, anonymous repository management, rebuttal drafting, and final camera-ready preparation. It provides backward-planning time offsets and risk identification.