Skill UI

A detailed playbook covering CRLF (Carriage Return Line Feed) injection techniques. This exploit targets insecure handling of user input within HTTP response headers (e.g., Location, Set-Cookie) or log files. It demonstrates advanced attacks like response splitting, XSS escalation via double CRLF, cache poisoning, and session fixation, essential for deep security testing.

A comprehensive guide to optimizing in-app paywalls, upgrade modals, and upsell features. This skill covers best practices for transitioning free users to paid subscribers, focusing on value demonstration, proper timing (after the 'aha' moment), and structuring conversion paths for maximum impact.

This guide provides a systematic, safe workflow for refactoring rapidly developed or AI-generated fullstack applications. It focuses on identifying and removing dead code, fixing broken imports, and consolidating repetitive logic while strictly maintaining existing API contracts, routes, and core functionality. Use this before launch to ensure a stable, maintainable production baseline, following a 'surgery, not demolition' philosophy.

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This skill provides a demonstration of interacting with external APIs and services using various command-line tools (CLI). It showcases core automation patterns, including fetching API documentation details and listing messages using specific application CLIs, making it ideal for testing and learning API integration flows.

This skill demonstrates how to exploit the Windows Data Protection API (DPAPI) to extract sensitive, encrypted secrets, including saved user credentials, browser logins, RDP passwords, and vault data. It covers online, offline, and domain-wide decryption techniques, essential for post-exploitation red team engagements and penetration testing.

This skill simulates high-impact container breakout techniques, demonstrating how to achieve root compromise on the underlying host operating system. It covers exploiting misconfigurations (e.g., privileged pods, hostPath mounts, exposed Docker sockets) and leveraging known vulnerabilities in runC/containerd, making it essential for authorized penetration testing and assessing the blast radius of a compromised microservice.

This guide demonstrates how to use the pycti Python client to model complex cyber threat intelligence within the OpenCTI platform. It structures threat actors, intrusion sets, campaigns, and TTPs into a unified STIX 2.1 knowledge graph. The skill is essential for correlating fragmented data from multiple sources (IOCs, reports) into a comprehensive, queryable adversary picture, supporting advanced threat hunting and detection engineering.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

Automates the creation and publication of polished, functional Gradio demos on Hugging Face Spaces for any user-provided LoRA model. This skill handles the complex workflow of designing the optimal UI, selecting the correct inference pipeline (e.g., text-to-image, video-to-video), and generating all necessary files (app.py, requirements.txt, README.md) to deploy a complete, ready-to-use AI playground.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.