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Systematic Debugging and Error Recovery

debugging-and-error-recovery

addyosmani/agent-skills

This guide provides a systematic, structured approach for debugging complex technical issues, such as failing tests, broken builds, or unexpected runtime errors. It teaches the 'Stop-the-Line' rule and utilizes a comprehensive triage checklist (Reproduce, Localize, Reduce, Fix, Guard, Verify) to ensure root causes are found and prevented from recurring.

Flutter Common Errors Debugging Guide

evanca/flutter-ai-rules

This guide provides comprehensive solutions for common Flutter runtime and layout errors, such as RenderFlex overflow, unbounded constraints, and improper state management (setState during build). It explains the cause, the specific error message, and provides actionable code fixes for various UI and state issues.

Systematic Debugging for Go Programs

golang-troubleshooting

samber/cc-skills-golang

A comprehensive guide for systematically diagnosing and fixing bugs, crashes, deadlocks, and performance issues in Go code. It covers debugging methodology, utilizing tools like Delve and pprof, detecting race conditions, and following the Golden Rules to find the root cause, ensuring robust and reliable Go development practices.

Universal PUA Motivator

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.

Performance Improvement Plan

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.

Generating Deep Links to Arize UI

github/awesome-copilot

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.

Hypothesis-Driven Debugging: Scientific Root Cause Analysis

hypothesis-testing

rohitg00/skillkit

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.

Mastering N8N Workflow Expressions Syntax

n8n-expression-syntax

sickn33/antigravity-awesome-skills

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.

Playwright Web Browser Automation Tool

playwright-skill

sickn33/antigravity-awesome-skills

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.

Optimize LLM Prompts with Trace Data

arize-prompt-optimization

github/awesome-copilot

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.

Export And Inspect LLM Traces And Spans

github/awesome-copilot

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.

Aspire Distributed Orchestration

github/awesome-copilot

Aspire is a code-first polyglot toolchain that orchestrates containers, executables, and cloud resources through an AppHost project, guiding creation, debugging, configuration, deployment, and troubleshooting for distributed applications across languages.

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