Skill UI

This skill provides bounded, local exploratory data analysis for diverse scientific data files, including CSV, JSON, HDF5, and image metadata. It rigorously audits data structure, detects missing values, and identifies outliers in a secure, sandboxed environment. It is designed exclusively for pre-modeling data integrity checks and must not be used to infer scientific meaning or make causal claims.

This guide provides comprehensive, version-aware documentation for using PufferLib in reinforcement learning pipelines. It details best practices for adapting Gymnasium/PettingZoo environments, managing different PufferLib versions (3.0.0 vs 4.0 source), and executing safe, reproducible training and evaluation routines. Key focus areas include environment contract validation, sandboxing native builds, and maintaining data provenance.

Seaborn is a high-level Python visualization library built on Matplotlib, designed specifically for creating publication-quality statistical graphics. It excels at dataset-oriented plotting, enabling users to perform complex multivariate analyses, visualize data distributions (like box plots, violin plots), and explore relationships with minimal code. It simplifies the process of generating multi-panel figures and statistical plots directly from pandas DataFrames.

A comprehensive workflow for setting up CI/CD pipelines for Flexport logistics integrations. This tool automates unit testing with mocked data, and executes live API contract validation against the Flexport sandbox upon merging to the main branch. It ensures data transforms and API compatibility across shipment lifecycle events, covering core logistics functions like booking, tracking, and customs documentation.

A comprehensive guide and implementation for configuring the CI/CD pipeline for AppFolio property management APIs. It utilizes a two-tier testing strategy: fast, mocked unit tests for quick feedback, and slower integration tests running against the AppFolio sandbox environment on main branch merges. This ensures robust validation of API interactions and prevents contract drift before production.

This skill establishes a comprehensive CI/CD pipeline specifically for Abridge clinical AI integrations using GitHub Actions. It enforces critical healthcare compliance steps, including FHIR R4 resource validation, robust detection of Protected Health Information (PHI) leaks (like SSN), and end-to-end sandbox integration testing. Ideal for deploying secure, compliant AI applications in the healthcare sector.

This setup provides a dedicated local development workflow for integrating with the AppFolio property management API. It allows developers to build and test complex integrations using sophisticated mock endpoints for rapid iteration, eliminating the need to consume live API quotas. Users can toggle between mock mode for swift iteration and sandbox mode for rigorous validation against the actual AppFolio Stack API, ensuring reliable and efficient development.

This debug bundle collects comprehensive diagnostic data from Apple Notes automation integrations. It captures system compatibility details, TCC permission statuses, account information, folder structure, and iCloud sync status. Use this tool to diagnose common issues like permission denials, sandbox restrictions, and scripting bridge errors that prevent Notes automation from working correctly on macOS.

This skill allows users to manage Box cloud storage as if it were a local filesystem. It supports two modes: transparent auto-syncing via hooks (on Write/Edit) and explicit operations using the Box CLI. It provides structured workflows for document management, collaborative syncing, and reliable version control.

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

A comprehensive development workflow solution for integrating with the Glean enterprise search API. It enables developers to simulate local search responses, mock data sources, and test custom connectors and UIs without needing a live Glean deployment. Users can toggle between mock mode for rapid iteration and sandbox mode for validation against a real Glean instance, ensuring a fast feedback loop for building robust enterprise search applications.

This guide details how to configure Grammarly API integration across multiple environments (Development, Staging, and Production). It ensures API credentials are isolated, style guides are managed per tier, and rate limits are respected. It provides setup instructions for mocking, sandbox testing, and robust deployment workflows.