Skill UI

This guide details the comprehensive deployment of Palo Alto Networks Prisma Access for implementing a modern SASE-based Zero Trust Network Access (ZTNA). It covers configuring the cloud infrastructure, deploying ZTNA Connectors, defining granular application policies, enforcing device posture via Host Information Profile (HIP), and utilizing GlobalProtect agents for secure, enterprise-grade remote access.

This skill covers performing comprehensive vulnerability assessments across diverse environments, including on-premises infrastructure, cloud VMs, and container registries. It leverages standard network protocols like SSH (Linux) and WMI (Windows), coupled with advanced cloud provider APIs for analyzing disk snapshots without requiring local endpoint agents. Ideal for security auditing and compliance checks.

Automatically reverse-engineers any codebase (frontend, backend, or fullstack) into a comprehensive Product Requirements Document (PRD). It analyzes complex elements like routes, components, API integrations, state management, and business logic across major stacks (React, Vue, Next.js, Django, FastAPI). The resulting PRD is detailed enough for both product managers (understanding 'what') and engineers/AI agents (understanding 'how' to reconstruct the system).

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

GoldRush API by Covalent lets agents query wallet balances, token prices, transaction history, DEX pairs, and live OHLCV across 100+ chains via REST, CLI, SDK, or WebSocket, avoiding chain-specific plumbing.

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

Register ICANN domains via a REST API with crypto payments so AI agents can autonomously check availability, pay in USDC/USDT/ETH/BTC, and retrieve DNS management credentials.

This agent facilitates comprehensive debugging and testing of iOS applications running on a simulated environment. It leverages XcodeBuildMCP to manage the entire lifecycle, including building, running, interacting with the UI (tap, type, gesture), taking screenshots, and capturing detailed runtime logs. Use it whenever you need to simulate real-world app testing or diagnose issues on a booted simulator.

This skill acts as an anti-over-engineering guardrail, instructing AI coding agents to maintain strict scope control and minimalism. It guides the AI to only modify the files and lines explicitly requested by the user, avoiding unnecessary abstractions, unrequested refactors, or scope creep. Use it when you prefer the simplest, most direct solution and want to prevent the agent from 'grinding' the code base.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

Blueprint turns a single objective into an executable, multi-step construction plan tailored for coding agents and multi-session teams, complete with context briefs, review gates, dependency awareness, and installable via `/blueprint <project> <objective>`.

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.