Skill UI

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

Methodology for assessing WPS-enabled routers using Pixie Dust offline recovery, online PIN brute force, lockout handling, and PBC edge cases. Useful for wireless security testing and understanding WPS exposure on consumer APs.

This utility serves as a Codex CLI alias, mapping specifically to the Claude Code `/pua:offline` command. It enables offline operation mode for developer tools by updating the user's configuration file (~/.pua/config.json). Use this for developers needing to simulate or operate in environments with limited connectivity, ensuring continued access to code assistance features.

A specialized assistant designed to navigate and accurately retrieve commands and parameters from the offline Alibaba Cloud CLI Chinese documentation mirror. It enforces a strict, hierarchical workflow: first consulting quick usage guides, then checking the main index, and finally drilling down to specific chapter pages. This structured approach ensures all provided information is accurate, evidence-based, and highly relevant to the user's intent.

This skill captures a fully self-contained static HTML snapshot from any running web application (e.g., React, Vue, Next.js). Using Puppeteer, it renders the live DOM, automatically inlining all CSS styles and converting images, assets, and backgrounds to base64 data URIs. This ensures the generated HTML file is completely self-sufficient and requires no external network connection, making it perfect for sharing UI states, creating documentation, or preparing assets for offline viewing.

This skill details how to abuse the Windows DPAPI (Data Protection API) to extract and decrypt sensitive secrets protected by the OS. It covers multiple scenarios: online decryption (using the target user's context), offline decryption (requiring passwords or NTLM hashes), and advanced domain-wide decryption via the DPAPI backup key. Essential for red-teaming and post-exploitation activities to harvest credentials, browser logins, and Wi-Fi passwords.

A powerful offensive suite for authorized red-teaming of Microsoft Entra ID (Azure AD). ROADtools combines ROADrecon for comprehensive directory enumeration (users, groups, policies, apps) and an offline GUI, with roadtx for advanced token acquisition, exchange, and Primary Refresh Token (PRT) abuse. It covers the full scope of cloud identity discovery and token-based attacks against highly regulated tenants.

Chainsaw is a fast, Rust-based forensic tool designed for first-response threat hunting. It analyzes collected Windows EVTX files using the comprehensive Sigma rule corpus, built-in detection rules, and high-speed keyword/regex searching. Furthermore, it uniquely analyzes critical artifacts such as shimcache, SRUM, and event-log gaps, providing robust, offline detection capabilities without needing a SIEM pipeline. It is essential for digital forensic triage and hypothesis confirmation.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.