Skill UI

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

This comprehensive guide serves as a technical reference for migrating applications from the Universal Windows Platform (UWP) framework to the modern WinUI 3 / Windows App SDK. It details crucial API changes, including namespace updates, window management (CoreWindow to AppWindow), threading models (CoreDispatcher to DispatcherQueue), and best practices for dialogs and background tasks. Essential for developers updating legacy UWP codebases.

This skill guides the systematic execution of a detailed, written development plan. It mandates a critical review phase to identify dependencies, vague requirements, and environment assumptions before starting. The process involves tackling tasks sequentially, performing rigorous verification (testing/checking), committing changes after each milestone, and systematically handling common exceptions like test failures or dependency gaps. It ensures a disciplined, traceable, and reportable development cycle.

This technique is used when multiple failure points (e.g., different test files or subsystems) are discovered, and these failures are independent of each other. Instead of debugging them sequentially, you dispatch separate, focused AI agents to work on each problem domain concurrently. This maximizes efficiency by allowing parallel investigation, dramatically reducing the time spent on large-scale debugging tasks.

This guide details the best practices for utilizing a code review sub-agent. It should be used after completing tasks, implementing major features, or before merging code into main branches. The process ensures that work results are validated against requirements, allowing reviewers to focus purely on the output rather than the development history, thus catching issues early.

Dask is a Python library that scales pandas/NumPy workflows beyond RAM by parallelizing DataFrames, Arrays, Bags, and custom tasks across cores or clusters for ETL, log analysis, and distributed ML.

Detects system CPU, GPU, memory, and disk availability before heavy scientific tasks, writing `.claude_resources.json` with strategic recommendations (parallelism, out-of-core, GPU acceleration) so analysts choose appropriate compute approaches.

This skill guides AI agents to handle complex technical tasks with maximum precision and minimal computational overhead. It implements adaptive verbosity rules, sophisticated input filtering (for logs, source files, VCS, and payloads), and surgical output generation. It ensures context-window awareness, structured reasoning, and adherence to best practices for coding, debugging, and architecture analysis.

This tool automates the complete specification generation process across four core phases: Initialization, Requirements, Design, and Tasks. It supports both interactive (step-by-step approval) and automatic execution modes. After generating the full spec, it performs a final sanity review to ensure coherence and completeness, providing a ready-to-implement blueprint.

This skill automates the process of transforming high-level product specifications, requirements, and design documents into a detailed, actionable, and executable task plan. It follows a multi-stage workflow, including context loading, parallel research, and two rigorous review gates (Task Plan Review and Task-Graph Sanity Review). The output provides properly sized (1-3 hours), logically sequenced tasks, ensuring every requirement is covered and dependencies are explicitly managed, dramatically improving project planning accuracy.

A comprehensive diagnostic reference for resolving common technical failures encountered when running web scraping and automation tasks on the Apify platform. Covers issues such as Actor run failures, API rate limits (429), authentication errors (401), anti-bot blocks, and memory allocation issues, providing structured fixes and best practices for robust scraping pipelines.

This comprehensive guide addresses common and complex errors encountered when automating Apple Notes on macOS using JXA or osascript. It covers three major areas: TCC permission denials, AppleEvent communication failures, and iCloud synchronization issues. The document provides a detailed error reference table, root cause analysis, tested fixes, and diagnostic scripts to help developers successfully automate Notes tasks.