Skill UI

This debug bundle provides comprehensive guidance for working with Together AI's OpenAI-compatible API. It supports critical LLM lifecycle tasks, including inference, fine-tuning, and model deployment for over 100 open-source models (e.g., Llama, Mixtral). It also includes detailed troubleshooting steps and solutions for common API errors (like 401 Unauthorized or 429 Rate limit), ensuring smooth development.

This internal agent skill defines the structured procedure for displaying bug triage results in terminal markdown format. It generates comprehensive summaries of bug clusters, provides detailed views of evidence and reports, and handles the parsing and execution flow for user review commands (e.g., `details`, `file`, `dismiss`, `escalate`). It is a core component for automated bug triage and defect management workflows.

This skill guides a systematic process for debugging reported issues. It explores the codebase to identify the root cause, traces the code path, and then generates a comprehensive GitHub issue. The fix plan strictly follows the Test-Driven Development (TDD) Red-Green cycle, ensuring proposed solutions are robust and durable, surviving major refactors.

This document outlines a comprehensive set of rules and guidelines for AI agents to ensure that relevant skills are called at every stage of interaction. It mandates the proactive use of skills—including brainstorming, systematic debugging, and specific process skills—before responding, clarifying, or accessing external resources, preventing assumptions, and enforcing structured, process-driven workflows.

This bundle provides comprehensive debugging guidance for Veeva Vault, covering REST API interactions, advanced VQL querying, and utilizing the VAPIL Java SDK. It helps developers manage document lifecycles, authenticate sessions, and resolve common operational and data access errors within the life sciences domain.

This utility collects comprehensive diagnostic evidence necessary for troubleshooting Webflow integrations and API usage. It automatically gathers crucial data points into a single, support-ready diagnostic bundle. Key metrics include the Webflow SDK version, API token validation status, rate limit headers, site connectivity reports, CMS health checks, and recent error logs, significantly streamlining the technical support process.

This module provides the core `Logger` abstraction for structured logging within the Webiny API context. It supports multiple logging levels (e.g., debug, info, warn, error), ensuring that developers can record application events and operational data systematically. Using this abstraction helps in centralized logging, which is crucial for debugging, monitoring, and maintaining system health.

A comprehensive guide for developers on deploying, developing locally, and managing environments for Webiny serverless platform on AWS. Learn how to use `yarn webiny deploy` for multi-environment releases (dev, staging, prod), and how to leverage `yarn webiny watch` for local API (Lambda stubs) and Admin (React HMR) development, ensuring full-stack debugging capabilities.

This debug bundle provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for various integration methods, such as low-latency WebSocket streaming and standard REST API calls. It is essential for developers building sophisticated voice-powered applications, dictation tools, or any system requiring robust speech recognition and error handling.

This debug bundle provides comprehensive guidance and core API endpoints for integrating the Workhuman Social Recognition and rewards system. It covers essential concepts like OAuth 2.0 authentication, peer-to-peer nominations, award levels, and bidirectional synchronization with major HRIS systems (e.g., Workday, SAP). Use it for building robust recognition workflows and debugging API interactions.

This skill ingests public complaints from X/Twitter, automatically detecting, classifying, and clustering reported bugs. It cross-references symptoms with repository evidence (commits, deploys) and assigns ownership using defined routing rules. It is essential for product teams needing to monitor product health and gather early bug signals from social media channels.

XcodeBuildMCP is a unified skill set designed to streamline the entire mobile and desktop development lifecycle. It replaces raw command-line tools like xcodebuild, xcrun, and simctl, providing structured workflows for iOS, macOS, watchOS, tvOS, and visionOS. Capabilities include project discovery, managing simulator and physical device workflows, advanced UI automation (gestures, view inspection), robust logging, and deep debugging using LLDB. It simplifies complex build, test, and run operations across multiple platforms.