Skill UI

This guide details how to build an MCP app, which is an MCP server enhanced to serve interactive UI resources. It allows developers to render complex widgets—such as forms, pickers, dashboards, and confirmation dialogs—directly inline within a chat conversation. Learn about the architecture, host specifics, and when to use a widget versus standard text output.

A specialized skill for generating production-ready, SEO-optimized visual assets. It handles various use cases including OG social cards, blog hero images, product visuals, and infographics. The system ensures outputs adhere to best practices like specific aspect ratios, schema markup, and file naming conventions, making content publishable and discoverable by search engines.

Provides Playwright-driven browser automation for scraping structured data, driving multi-step workflows, managing sessions and anti-detection tactics, and capturing screenshots/PDFs in production pipelines.

ADHX provides a dedicated API to fetch X/Twitter posts as clean, structured JSON. It extracts comprehensive data, including author profiles, engagement metrics (likes, retweets), and full article content for long-form posts. This method is vastly superior to traditional scraping techniques, making it ideal for LLM analysis, summarization, and structured data workflows.

Mentors founders through the Minimalist Entrepreneur method for landing the first 100 customers: skip the launch, work concentric outreach circles, craft pricing, capture manual sales metrics, and set weekly goals before celebrating.

A specialized skill designed for comprehensive literature reviews in the communications and networking domain. It supports topics including 5G/6G, Wi-Fi, satellite communications, routing, and cross-layer optimization. The skill employs a knowledge-base-first retrieval strategy, prioritizing local sources (Zotero, Obsidian) before searching major academic databases (IEEE, ACM) to ensure depth and relevance.

A comprehensive knowledge-base-first skill designed for deep literature reviews in advanced communication domains. Use it for topics like 4G/5G/6G, wireless networking, satellite communications, Wi-Fi, routing protocols, and cross-layer optimization. It systematically searches structured academic databases (IEEE, ACM, Zotero, etc.) to provide related work, survey papers, and landscape summaries.

A comprehensive skill for conducting authorized Bluetooth Low Energy (BLE) security assessments. It covers detecting various attacks, including sniffing, Man-in-the-Middle interception, replay attacks, and GATT enumeration abuse. The workflow guides users through packet capture using specialized sniffers (Ubertooth, nRF Sniffer), analyzing services and characteristics via Python/bleak, and performing cryptographic analysis using tools like crackle.

This guide details how to analyze comprehensive Zeek network logs (conn.log, smb_files.log, ntlm.log, dce_rpc.log, etc.) to detect advanced attacker techniques. It specifically targets indicators of compromise related to lateral movement, such as SMB admin share access, NTLM account spraying, and remote service execution (DCE/RPC patterns). It is a critical network forensics tool for post-incident investigation.

This system detects ransomware by combining multiple advanced techniques: real-time entropy analysis, file system I/O monitoring, and behavioral heuristics. It identifies abnormal mass file modification patterns, sudden spikes in data entropy (indicating encryption), and suspicious process activities like shadow copy deletion. Ideal for building advanced EDR detection layers to catch zero-day and unknown ransomware variants.

This tool detects malicious packages mimicking legitimate dependencies in npm and PyPI. It analyzes package names using Levenshtein distance, examines publish date heuristics, and identifies download count anomalies to secure the software supply chain. Ideal for auditing project dependencies and proactively monitoring for supply chain threats.

Trackio is a comprehensive library for logging, visualizing, and managing ML experiment metrics and alerts. It allows developers to use a Python API to log metrics and set diagnostic alerts (INFO/WARN/ERROR) during training. Furthermore, a dedicated CLI enables metric retrieval and dashboard viewing, syncing data to Hugging Face Spaces for persistent, remote monitoring, making it ideal for autonomous ML agent workflows.