Skill UI

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

This skill provides the core mechanism for integrating Hex into external data pipelines. It enables triggering, monitoring, and managing Hex projects (e.g., via Airflow or Dagster) programmatically. Users can implement parameterized runs, execute multi-step ETL workflows sequentially, and handle status polling and error management, making it ideal for production data orchestration.

This workflow automates the extraction of key insights from meeting recordings using Fathom transcripts and summaries. It builds a robust pipeline to identify action items, sync tasks to project management tools, and analyze overall meeting patterns, streamlining post-meeting follow-up and record-keeping.

A comprehensive project management guide for submitting database systems papers to EDBT. It details the complexities of the multiple-cycle rolling publication model, covering everything from initial evidence lock and cycle selection to the five-step review arc (Submission, Author Feedback, Revision, etc.). Use this to backward-plan your research timeline and manage the entire academic publishing process.

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.

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 project management guide for planning submissions to KDD, accounting for its unique structure of two annual submission cycles. It covers milestone tracking, cycle-choice decision matrices, managing the Resubmit loop, and planning evidence lead times from abstract to camera-ready status. Essential for complex, multi-stage data science research.

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.