Skill UI

Read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data with pydicom 3.0.2. Covers metadata, transfer syntaxes, compression plugins, frames, private elements, JSON export, and bounded de-identification review with strict PHI safety boundaries.

Create truthful, accessible, publication-ready scientific figures using Matplotlib, Seaborn, or Plotly. Ensures data integrity, proper encoding, accessibility compliance, and journal export planning for research publications. Includes guidelines for uncertainty displays, color/contrast review, and metadata validation.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

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.

A comprehensive reference architecture detailing how to automate Apple Notes on macOS. Since there is no public REST API, this system relies on a layered approach using Node.js, JXA scripting via osascript, local SQLite caching, and FSEvents polling. It outlines best practices for creating robust, local automation solutions for note management and data export.

This comprehensive guide outlines essential security best practices for developing automation scripts that interact with Apple Notes on macOS. It covers critical areas such as managing TCC permissions, preventing data leakage, securely exporting content, and handling end-to-end encrypted notes (locked notes). Use this resource when building robust, secure, and compliant applications within the Apple ecosystem.

This guide provides strategies to optimize the usage and costs associated with the Canva Connect API. It covers best practices such as aggressive metadata caching, implementing export tracking to prevent duplicates, managing pagination efficiently, and monitoring API call volume to stay within rate limits and budget constraints.

A comprehensive workflow designed to build deep revenue analytics dashboards using exported Clari data. It calculates key metrics such as forecast accuracy, pipeline coverage ratios, and tracks representative performance changes. Ideal for financial analysts and sales managers needing to assess revenue health and forecast reliability.

This guide provides advanced methods to significantly boost Clari API performance and data pipeline efficiency. It covers parallelizing multi-period data exports using Python's concurrency features, implementing a robust local caching mechanism with Time-To-Live (TTL), and optimizing data warehouse loading using SQL MERGE statements. Use this when facing slow exports, high API call volume, or requiring fast data synchronization in multi-forecast environments.

This skill provides robust mechanisms to interact with the Clari API, specifically designed to handle rate limiting (HTTP 429 errors) and manage asynchronous bulk export jobs. It implements exponential backoff and sequential scheduling, ensuring reliable data retrieval and forecasting exports without hitting API limits. Ideal for automated data pipelines and background job processing.

A collection of production-ready client implementation patterns for interacting with the Clari v4 API, provided in both Python and TypeScript. This guide covers essential functionalities like listing and exporting forecast data, implementing job polling, and managing complex data transformation pipelines, ensuring reliable and reusable integration for development teams.

This skill provides a robust solution for monitoring critical forecast changes within Clari. Since Clari lacks native real-time webhooks for all data, this system implements change detection by polling periodic data exports and comparing current snapshots against historical records. It calculates the percentage change and can trigger immediate alerts via platforms like Slack, ensuring users are always aware of significant shifts in revenue forecasts or sales pipelines.