Skill UI

A comprehensive guide to mastering AI-powered coding assistance within the IDE. Learn to use the chat panel for conversational debugging and architectural discussion, and utilize inline editing for surgical code refactoring. Features advanced context referencing using @-mentions (e.g., @Files, @Codebase) to improve code understanding and generation accuracy.

This bundle provides a comprehensive framework for diagnosing and fixing common issues when AI code generation is incorrect within Cursor. It systematically covers the root causes of poor suggestions, including context mismanagement (missing, wrong, or overflowing context), model hallucinations, and ambiguous prompting. Use this guide to improve the accuracy and reliability of AI-assisted coding workflows.

This is a hands-on walkthrough demonstrating the four core AI features of the Cursor IDE: Tab Completion, Chat, Inline Edit, and Composer. Users learn how to leverage AI for coding tasks, debugging, generating boilerplate code, and performing multi-file refactoring within a structured project environment. It's an essential guide for developers adopting AI-assisted coding workflows.

A comprehensive toolkit for advanced debugging and systematic incident response within Customer.io. It provides structured playbooks, API debug clients, and investigation scripts to diagnose complex delivery issues, analyze user profiles, or troubleshoot campaign failures, ensuring robust incident management.

A comprehensive debugging bundle designed to collect all necessary evidence for Customer.io support tickets. It performs API connectivity diagnostics (Track/App), validates user profiles, captures environment details (Node.js, SDK versions), and generates a structured report. Ideal for investigating delivery failures or complex integration issues.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

A comprehensive, expert-level workflow for debugging complex software issues in production environments. This skill guides users through initial triage, advanced observability data collection (APM, distributed traces, logs), intelligent hypothesis generation, and full root cause analysis. Use it when dealing with intermittent failures, performance bottlenecks, or needing a complete fix and prevention plan.

This expert guide provides a systematic, structured methodology for debugging complex software issues. It teaches users how to analyze stack traces, correlate logs, establish consistent reproduction steps, and apply hypothesis-driven testing to isolate and resolve bugs, crashes, or unexpected behavior in any codebase. Essential for professional troubleshooting and quality assurance.

A comprehensive guide for diagnosing and fixing common Deepgram API errors. Covers HTTP error codes, WebSocket issues, optimal audio preparation using ffmpeg, and implementing robust retry patterns for reliable Speech-to-Text integration.

Collects comprehensive, sanitized debug evidence for Deepgram support and troubleshooting. This bundle gathers detailed system environment information (Node.js, Python), performs live API connectivity tests, analyzes audio file metadata using ffprobe, and logs request/response data. Essential for preparing detailed support tickets and reproducing bugs related to Deepgram integration.