Skill UI

This specialized tool analyzes Git diffs against a stated development intent to detect scope creep. It identifies overly broad pull requests, unrelated files, dependency additions, public API renames, or configuration changes. It guides users on whether changes should be kept, split into separate commits, or if the connection requires explicit justification, ensuring clean and focused code merges.

A comprehensive guide for packaging and evaluating research artifacts for academic conferences like SIGMETRICS. It details the requirements for achieving ACM Badges (Functional, Reusable, Reproduced) by ensuring the packaged simulation can regenerate key figures and results on a clean machine, moving beyond mere tool execution.

This guide outlines the rigorous process for submitting and evaluating research artifacts for academic conferences (TACAS/ETAPS). It details the mandatory two-round evaluation system, covering tool-demonstration papers and general research case studies. Authors must package their work to achieve badges like Available, Functional, and Reusable, ensuring maximum reproducibility and environmental compatibility on a clean virtual machine.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

A comprehensive guide for optimizing computer science manuscripts targeting the USENIX Annual Technical Conference (ATC). It covers core systems research fields (OS, networking, storage, security), defining the required evidence bar (built artifacts, clean evaluation), and re-framing the paper's contribution to align with top-tier systems venue expectations.

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

This skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.

Automates the creation of isolated Git worktrees for safe and efficient feature development. This process handles branch validation, optimizes dependency installation (e.g., symlinking node_modules), runs background verification tests, and provides guidance for database isolation, ensuring developers always work in a clean, separated environment.

A robust tool for batch cleanup of stale or merged Git worktrees. It automatically identifies and safely removes merged branches, calculates precise disk usage per worktree, and provides an interactive review process for unmerged or protected branches. Essential for maintaining clean repository environments.

This tool provides a comprehensive and safe mechanism to remove a git worktree. It includes multiple safety checks, such as verifying if the branch is protected, checking for uncommitted changes, and confirming merge status. Furthermore, it guides the user through the entire cleanup process, including deleting local and remote branches, pruning references, and reminding about associated database cleanup.

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

This skill processes raw mentions of conditions and diagnoses extracted from clinical notes. It performs synonym deduplication, filters out negated or hypothetical mentions, and assigns a structured clinical status (active, resolved, or historical). The output is a clean, reconciled problem list, structured for USCDI Problem or FHIR Condition elements.