Skill UI

A comprehensive guide to optimizing context window usage within AI coding environments like Cursor. Learn how to strategically use @-mentions, manage context pills, and leverage different context sources (codebase, documentation, web) to prevent context overflow and ensure the AI generates highly accurate, relevant, and context-aware code suggestions and answers for complex development tasks.

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

This skill leverages Google Gemini's advanced image generation model to create high-quality, context-specific visuals. It is essential for developers needing placeholder images for UIs, generating detailed diagrams for technical documentation, or visualizing abstract concepts, logos, and assets across cross-platform projects (Windows, macOS, Linux).

Flash Attention optimizer delivers 2-4x speedup and 10-20x memory savings for transformer attention, ideal when training or running long-context models that hit GPU memory or latency limits; supports PyTorch native scaled_dot_product_attention, the flash-attn library, H100 FP8 precision, and sliding-window attention.

This skill provides advanced context management for AI in large-scale, complex codebases. It optimizes context windows, ensures memory persistence across multiple sessions, and helps prioritize critical information. Use it when working with massive code repositories to maintain context and improve developer workflow.

A comprehensive guide for developers encountering complex issues with the Claude API, including context window overflow management, debugging tool use failures, handling streaming corruption, and diagnosing unexpected stop reasons. Provides Python code snippets and diagnostic commands to ensure robust AI integration.

A comprehensive guide for diagnosing and fixing common errors encountered when integrating with the Anthropic API (Claude). It covers critical issues such as authentication failures (401), rate limiting (429), server overload (529), invalid requests, and context window limits. Includes practical code solutions and best practices for robust API integration.

Provides comprehensive guidelines for integrating the Wispr Flow voice-to-text API into development environments. This skill covers both low-latency WebSocket streaming and simpler REST endpoint usage. It is essential for building voice-powered applications, including real-time dictation, command-line interactions, and context-aware code transcription across multiple platforms (Mac, Windows, iOS).

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

This skill provides a structured, multi-step guide for automating interactions with desktop applications. It emphasizes obtaining a reliable context via observation, preferring UI Automation (using element IDs for buttons, menus, or editable fields) over raw coordinates. It covers best practices for keyboard input and ensuring state consistency across major operating systems (Windows/macOS), making it suitable for complex, reliable process automation.

This skill automatically wraps high-verbosity shell commands (such as git log, cargo test, pytest, or find) using an RTK wrapper. It detects excessive CLI output and executes optimized commands, dramatically reducing the number of tokens consumed in the context window. Use this when standard command output is overly verbose and wasting valuable context tokens.