Skill UI

A comprehensive security auditing framework designed to systematically detect sensitive information disclosure from Large Language Models (LLMs). It uses multi-phase, escalating dialogue probes to test for leaks such as system prompts, API keys, PII, and internal configurations. Essential for red teaming and rigorous model security assessment.

This skill acts as a rigorous, Socratic interviewer designed to stress-test a proposed plan against an established domain model and existing project documentation. It forces the user to sharpen terminology, resolve cross-cutting dependencies, and identify inconsistencies between stated requirements, code implementations, and documented decisions. It ensures the resulting architecture is precise, consistent, and well-documented by automatically updating the context and generating Architecture Decision Records (ADRs).

This skill acts as a specialized prompt engineer for AI design tools. It transforms vague, rough UI/UX ideas into polished, highly optimized prompts. It systematically enhances prompts by injecting missing details such as specific components, design system adherence (colors, typography), page structure, and platform context, ensuring the resulting command is professional and ready for superior AI generation.

FastMCP CLI provides a unified command-line interface to interact with Model Context Protocol (MCP) servers. It allows users to discover available tools and invoke them programmatically using `fastmcp list` and `fastmcp call`. The CLI supports various server targets (HTTP URLs, local files, configs), complex argument types, and ensures structured output, making it essential for building automated, multi-tool workflows and system integrations.

This skill serves as a comprehensive entry point for initiating new development work. It systematically analyzes project metadata (specs, roadmaps) to determine the optimal work decomposition path (new spec, existing extension, or simple fix). By conducting deep context loading and guiding structured dialogue, it refines vague ideas into clearly defined, actionable scope boundaries. Finally, it proposes multiple concrete technical approaches, complete with trade-offs and estimates, ensuring the resulting project scope is well-defined and ready for implementation.

This skill orchestrates the entire development lifecycle for approved software tasks. It operates in autonomous or manual modes, rigorously applying Test-Driven Development (TDD). It automatically gathers all necessary context (specs, requirements, design, and historical learnings), validates the repository state, and dispatches specialized sub-agents to implement tasks one by one, ensuring high code quality, adherence to design, and process traceability.

This guide provides production-ready, idiomatic patterns for interacting with Snowflake using official SDKs (Node.js and Python). It covers essential patterns like implementing robust connection pooling, handling asynchronous query wrappers, efficiently streaming large result sets, and utilizing context managers for reliable resource cleanup. Use this when establishing team coding standards or optimizing data access performance.

This guide outlines best practices and structured workflows for automating complex tasks within the Unity Editor using the Model Context Protocol (MCP). It provides programmatic methods for managing GameObjects, applying script edits, analyzing the editor state, executing batch commands, and capturing detailed, multi-view screenshots. Ideal for developers needing robust, repeatable, and automated testing or operational tasks within Unity projects.

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.

This module provides core abstractions for handling long-running, asynchronous background tasks within an API context. It introduces `TaskDefinition` to define the complete lifecycle and hooks for a background task, and `TaskService` to programmatically trigger and manage these operations. It is essential for backend development requiring robust, non-instantaneous processing.

A persistent knowledge base that transcends standard RAG by integrating three core layers: raw source storage, a structured knowledge graph, and a dynamic wiki layer. It ingests documents once, automatically extracting entities and relations to build a comprehensive, cross-referenced, and verifiable knowledge graph. Ideal for enterprise research, complex Q&A, and deep knowledge accumulation.

A systematic auditor designed to evaluate the resilience and quality of complex AI workflows. It scans across five critical dimensions—Prompt Quality, Context Efficiency, Tool Health, Architecture Fitness, and Safety—providing a scored report and prioritized, actionable remediation steps. Use this tool to find hidden flaws and ensure robustness before deploying any AI agent or system.