Download

Skill UI

Browse and discover 11130+ curated skills

All Development Artificial Intelligence Design & Creative Product & Business Data Science Marketing Soft Skills Productivity Engineering Languages

Search Bug , found 527 results

Default Newest Most Downloaded

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.

Test Driven Development Cycle

test-driven-development

addyosmani/agent-skills

Test-driven development requires writing a failing test before any implementation so logic, bug fixes, and behavioral changes are always backed by proof; it is ideal for guarding refactors and ensuring regressions stay caught by the automated suite.

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.

Manage And Automate GitHub Issues

github/awesome-copilot

This skill provides comprehensive capabilities for managing the entire issue lifecycle on GitHub. Users can create structured bug reports, feature requests, and tasks, and also read, list, and update existing issues. It supports setting labels, assigning owners, managing milestones, and tracking complex dependencies. Ideal for engineering teams needing robust project backlog management.

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.

Systematic Root Cause Analysis

root-cause-analysis

rohitg00/skillkit

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

AI-Powered Web Application Testing

github/awesome-copilot

Leverage AI-powered exploratory testing to thoroughly audit web applications and verify functionality. This skill simulates user interaction, detecting complex bugs, validating critical flows (like checkout or login), and checking accessibility compliance. Use it proactively after development to ensure quality before deployment.

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.

Prev 1 2 3...20 21 222324 25 26...42 43 44 Next

Language