Skill UI

This tool enables deep inspection and validation of web applications by connecting to the browser's DevTools Protocol (MCP). It allows agents to perform tasks like DOM inspection, capturing console logs, monitoring network requests, profiling performance, and taking screenshots in a live environment. Essential for debugging UIs, validating API integrations, and performing end-to-end automated testing.

Guides systematic root-cause debugging when tests fail, builds break, or behavior unexpectedly changes. It covers stop-the-line discipline, a triage checklist, git bisection for regressions, minimal reproduction, root-cause fixes, regression tests, and end-to-end verification. Ideal for turning confusing errors into reliable recovery workflows.

Provides solutions for common Flutter runtime and layout errors, including RenderFlex overflow, unbounded constraints, unbounded width, setState during build, ScrollController conflicts, and RenderBox layout issues. Includes debugging tips and constraint fixes for developers.

A comprehensive guide for systematically debugging Golang programs. This skill helps users find and fix the root cause of bugs, crashes, deadlocks, race conditions, and performance bottlenecks. It covers advanced techniques like pprof profiling, using Delve, implementing test-driven debugging, and follows a rigorous methodology to ensure deep root cause analysis before proposing any fix. Ideal for production environments and complex concurrency issues.

OpenViking Memory enables AI agents to recall and persist long-term semantic knowledge using MCP tools. It supports retrieving prior context at task start via find/search/read and storing durable facts, preferences, and lessons via remember. Ideal for coding, debugging, and multi-step workflows, ensuring continuity across sessions without automatic lifecycle hooks.

A universal motivation skill that pushes an AI to stop stalling, search and verify proactively, and pursue end-to-end solutions for debugging, research, writing, planning, deployment, and more.

PIP kicks in when tasks fail repeatedly or the assistant becomes passive, layering Western big-tech performance rhetoric, a systematic debugging methodology, and proactivity enforcement so every code, ops, research, deployment, or writing task is owned, verified, and driven to completion.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

This is an expert guide for writing, debugging, and validating complex expressions within n8n workflows. It covers core variables such as $json, $node, $now, and $env, detailing how to correctly access nested data from current nodes, previous nodes, and webhook payloads. Learn best practices for handling data structures, managing variable scopes, and avoiding common syntax errors to ensure reliable workflow automation.

A general-purpose skill for writing and executing end-to-end (E2E) browser automation tests using Playwright. It handles complex scenarios such as login flows, form submission, responsive viewport testing, and checking for broken links. The skill automatically detects local development servers and executes custom scripts in a safe, temporary directory, providing real-time debugging and results visualization.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.