Skill UI

This skill helps developers accurately find and explore the correct Windows API for specific platform capabilities, such as file access, camera usage, notifications, or UI controls. It searches a comprehensive local cache of WinMD metadata, providing full technical details including methods, properties, events, and enumeration values for APIs sourced from the Windows Platform SDK and WinAppSDK. It is indispensable when building complex features requiring deep integration with the Windows operating system.

A comprehensive skill designed to convert vague ideas, initial concepts, or high-level requirements into structured, detailed, and approved technical specifications. It enforces a rigorous, multi-step workflow, guiding users through context exploration, proposing multiple architectural trade-offs, and iteratively refining the design until a complete specification document is ready for the implementation phase.

This guide teaches advanced search techniques for identifying specific video games. It emphasizes using highly discriminative technical details such as specific platforms (e.g., DOS, NES, PS4), distribution methods (shareware), game engines, era, and detailed review scores from niche sites. By combining multiple constraints (Platform + Era + Detail), users can dramatically narrow down search results, even locating specific developers or artists involved in the game's creation.

This comprehensive tool guides the systematic creation of detailed technical specifications. It orchestrates the entire design lifecycle, including context loading, deep discovery and analysis (tailored for new features, extensions, or simple additions), synthesis using established design principles, and generating a boundary-first, structured design draft. Ensures all requirements are mapped to explicit technical components.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.

This technical guide provides patterns for handling rate limits, throttling, and concurrency issues when scraping data using Bright Data services. It demonstrates implementing concurrency control using queuing, applying exponential backoff for transient errors (like 429 or 503), and managing specific API trigger rate limits (e.g., 20/min, 60/hr). Essential for building robust, large-scale web scrapers.

A comprehensive guide detailing 10 common mistakes and best practices when integrating with the Anthropic API (Claude). It covers critical technical aspects such as correctly setting `max_tokens`, using the top-level `system` parameter, managing message alternation, checking `stop_reason`, and optimizing client initialization for robust AI application development.

This skill provides a comprehensive, multi-phase guide for authors to draft a Specification Enhancement Proposal (SEP) adhering to rigorous governance standards. It walks users through prerequisites, initial vetting (Gate), detailed interviews (e.g., breaking changes, security), and technical research, ensuring the proposed change is viable and community-vetted before formal submission.

A comprehensive guide to diagnose and resolve common technical errors encountered when integrating with or using the Fathom API. It covers specific issues such as 401 Unauthorized, 429 Rate Limiting, empty transcripts, and webhook failures, providing actionable fixes for developers and power users.

Generates high-quality, deterministic, and editable vector diagrams (SVG) for system architecture, workflows, and pipelines. By using a structured JSON specification (FigureSpec), it ensures that the same input always yields the identical output, making it ideal for reproducible technical documentation, academic papers, and formal system modeling where precise layout and connections are critical. It is optimized for non-data-plot, formal diagrams.

This skill provides comprehensive guidance and solutions for diagnosing and fixing common technical errors and exceptions encountered when integrating with the Grammarly API. It covers specific HTTP error codes (400, 401, 413, 429), addressing issues such as invalid tokens, payload size limits, rate limit exhaustion, and text formatting requirements. Use it when debugging failed API requests or troubleshooting complex integration workflows.