Skill UI

The Form Model system provides a comprehensive, declarative way to build complex user forms. It supports defining various field types (Text, Number, DateTime, File, Object, etc.) using a fluent builder API. Developers can control layout, apply advanced validation (Zod), implement conditional visibility/disability rules, and handle complex data structures like nested objects and dynamic zones, making it ideal for building advanced admin interfaces.

This extension provides comprehensive distributed tracing capabilities for the KubeSphere Observability Platform, adhering to the OpenTelemetry standard. It guides users through the entire lifecycle, including component installation (Generator, Collector, Agent), configuration, and querying complex tracing data, spans, and service graphs. It is essential for monitoring microservices and understanding complex system interactions.

A powerful skill that enables natural-language interaction with complex DeFi protocols across multiple EVM chains. It acts as a non-custodial agent, interpreting user prompts (e.g., 'swap X for Y') into simulated, multi-step transactions (like approve + swap). It utilizes account abstraction (EIP-7702/ERC-4337) to simulate and queue secure, user-signed transactions via the CLI, making advanced DeFi accessible from the terminal.

MockHunter is an advanced skill designed to audit live web pages, providing a comprehensive reality check on visible data. It analyzes every element to determine its true source: whether it is REAL (from the database), MOCK (simulated data), LLM-generated, HARDCODED, BROKEN (broken endpoints), or UNKNOWN. Ideal for auditing AI-generated UIs or reviewing proof-of-concept MVPs before customer sign-off.

This skill provides a mechanism to execute TypeScript scripts using tsx, enabling fetching and streaming responses from external HTTP endpoints. It is designed for users who need a lightweight Node.js helper utility to manage simple network API calls, making it ideal for rapid integration and data fetching tasks.

This CLI alias executes a comprehensive teardown protocol for PUA agents, designed for development environments. It safely releases all active agent and loop states, ensuring a clean system reset. It automatically manages configuration files like ~/.pua/config.json, preserving unknown fields while providing reliable, evidence-based cleanup.

This skill guides the safe execution of chaos engineering experiments to surface weaknesses in production systems without causing actual outages. It provides tools to design robust experiments by defining hypotheses, calculating the potential 'blast radius' before failure injection, setting strict abort criteria, and generating structured postmortems. Ideal for SRE teams running game days and resilience tests.

A structured methodology for validating non-trivial decisions by subjecting them to an adversarial, fresh-context review. This process is crucial when correctness is paramount—such as in production, security-sensitive logic, or complex architecture—by forcing a skeptical examination that aims to disprove assumptions rather than simply approve them.

A comprehensive toolkit for managing the entire lifecycle of feature flags, treating them as a controlled process rather than simple if-statements. This system provides scripts to scan for technical flag debt, plan progressive rollouts (e.g., ring, linear), and audit for crucial kill-switches. Ideal for release engineering, ensuring features are properly launched, tested, and safely retired.

This CLI alias acts as a dedicated command to disable the PUA always-on mode feature, typically used within AI coding environments like Claude Code. By modifying the user's configuration file (`~/.pua/config.json`), it sets `always_on=false` and `feedback_frequency=0`. This function provides users with granular control, effectively turning off continuous monitoring or background feedback mechanisms managed by the AI assistant.

A comprehensive pre-production audit tool that scans a codebase for critical issues across eight dimensions: security, database, deployment, code quality, AI/LLM usage, dependencies, frontend, and observability. It intercepts deployment commands, forcing users to resolve critical failures before shipping code. The audit is stack-agnostic and provides detailed pass/fail/manual reports.

This guide assists developers in building robust Model Context Protocol (MCP) servers and clients using C#/.NET. It specifically targets the stable 1.x NuGet packages, providing detailed instructions for configuring various transports (STDIO, Streamable HTTP), defining custom tools, prompts, and resources. It helps solve common integration issues like stale preview pinning, complex HTTP wiring, and structured content handling, ensuring production-ready, standard-compliant code.