Skill UI

Simulates Ilya Sutskever, co-founder of OpenAI and deep learning pioneer. Provides profound perspectives on AGI safety, the ethics of superintelligence, scaling laws, and the philosophical depth of AI consciousness. Ideal for complex, safety-critical deep technical discussions.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Creates photorealistic or stylized images via Stability AI (SD3.5/Ultra/Core) with text-to-image, img2img, inpainting, upscale, background removal, erase, and search-replace modes for artistic or production-ready output.

Manages and coordinates complex workflows involving multiple AI agents and tasks. It efficiently distributes work across multiple workers, handles runtime errors, and intelligently merges disparate results into a unified output, making it ideal for large-scale, distributed AI computations.

This skill provides a structured methodology and Python implementation for monitoring and analyzing data leak sites (DLS) associated with ransomware groups. It helps security analysts track victim postings, extract threat intelligence on group tactics, assess sector-specific risks, and understand evolving ransomware threat landscapes for proactive defense.

This tool provides a comprehensive framework for analyzing web server logs (Apache/Nginx) to detect various security threats. It uses advanced regex matching against OWASP attack signatures to identify SQL injection, Local File Inclusion, and Directory Traversal attempts, alongside behavioral analysis for brute-force attacks and web scanner fingerprints. The system also enriches data using GeoIP for source attribution, generating a prioritized report for SOC analysts.

This tool automates the end-to-end workflow for GDPR Data Subject Access Requests (DSAR). It handles identity verification, discovers PII across structured and unstructured data sources using NER and regex, maps data to Article 15 requirements, and generates auditable compliance reports, ensuring adherence to ICO/EDPB guidelines and scaling compliance efforts.

This skill utilizes AI/LLM reasoning capabilities combined with industry-standard OSINT tools (Sherlock, theHarvester, SpiderFoot). It correlates fragmented data—including usernames, emails, domain records, and breach data—from multiple sources into unified, confidence-scored intelligence profiles. It is ideal for deep target profiling, identity resolution, and mapping complex threat landscapes.

A comprehensive meta-skill template designed for developers to scaffold, structure, and define specialized Agent Skills for AI platforms like GitHub Copilot. It guides users through mandatory requirements, including folder naming conventions, YAML frontmatter best practices (name, description), and structuring the skill body with detailed workflows and documentation sections. Use this when building complex, specialized AI capabilities.

Transforms the AI into a highly personalized virtual tutor, adopting a defined persona, teaching style, and emotional relationship. This skill uses the Socratic method and advanced psychological principles (like Self-Determination Theory and memory scaffolding) to guide users through discovery learning. It doesn't just deliver answers; it fosters genuine engagement, maintains continuity across sessions, and motivates the user to learn by simulating the experience of a caring, challenging human mentor.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining flexibility. It automates training workflows, supports multi-GPU/TPU orchestration, and implements best practices for neural network training. Features include LightningModules, Trainers, data pipelines, callbacks, logging (W&B, TensorBoard), and distributed training strategies like DDP and FSDP for scalable training.