Skill UI

This internal agent skill defines a systematic, multi-step procedure for scanning GitHub repositories to gather comprehensive evidence supporting or explaining bug clusters. The process covers searching open issues, inspecting recent commits, analyzing code paths, and checking deployment history. It assigns evidence confidence tiers (Exact, Strong, Moderate, Weak) to provide a deep, actionable understanding of potential software defects.

This skill synthesizes a comprehensive Product Requirements Document (PRD) from the current conversation context and understanding of the codebase. It guides the user through structuring the problem statement, defining detailed user stories, outlining implementation decisions, and suggesting test strategies. The final PRD is formatted and submitted directly as a GitHub issue, ensuring a structured handover from discussion to development specification.

This pattern provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for both low-latency WebSocket streaming and simpler REST API usage. Key topics include authentication, recommended audio formats, robust error handling, and context awareness (e.g., understanding code or CLI commands), making it ideal for building advanced voice-powered applications.

Use this skill to prompt the agent to zoom out from a specific code section, providing a broader, higher-level perspective. It helps map out the overall system architecture, identifying relevant modules, calling relationships, and how the current code snippet fits into the larger picture. Ideal when you are unfamiliar with a specific area or need system-level understanding.

This guide establishes best practices for code instrumentation and system observability. Learn how to implement structured logging, metrics (RED), and distributed tracing to make production behavior fully diagnosable. It is essential for any feature deployed live, ensuring rapid incident response and deep system understanding.

This comprehensive guide details idiomatic best practices for writing high-performance, safe code in Rust. It covers advanced topics such as understanding Ownership and Borrowing rules, robust error handling using the '?' operator, optimizing functional programming with Iterators, implementing structured concurrency, and defining robust data models with Structs and Enums.

This skill is a comprehensive tool for managing and understanding SQL queries. It can explain complex SQL in plain English for non-technical stakeholders, optimize slow queries by identifying performance bottlenecks, generate functional SQL code from natural language prompts, and produce detailed query documentation and data dictionaries. Supports major dialects including PostgreSQL, MySQL, and BigQuery.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

This powerful skill provides an interactive debugger interface, allowing developers to set breakpoints, step through source code line by line, inspect live variable states, and trace the entire call stack. It is essential for diagnosing complex bugs, understanding unexpected program flow, or determining the root cause when standard logging fails. It supports various languages via the DAP protocol.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.

Provides deep semantic code intelligence for Go projects using the official `gopls` language server. Key capabilities include jumping to definitions, finding references, visualizing call graphs, and performing sophisticated refactoring (extract, inline, rename). It is essential for navigating large Go codebases, understanding package dependencies, running diagnostics, and ensuring safe code modifications.

This tool is designed to analyze source code by extracting core entities (classes, functions, modules, types) and mapping their complex relationships (imports, inheritance, calls, dependencies). It constructs a comprehensive Knowledge Graph (KG) in AgentDB. This is essential for detailed dependency analysis, understanding large codebase structures, and performing accurate impact assessments.