Skill UI

This guide details the standardized directory structure and best practices for building robust and maintainable plugins for Claude Code. It covers critical components including the plugin manifest (plugin.json), commands, agents, skills, and hooks. Use this skill when you need to scaffold a new plugin, understand the required component organization, or configure the plugin's architecture according to best practices.

Provides a catalog of Linux and Windows post-exploitation privilege escalation techniques, covering sudo/capability abuse, scheduled tasks, token impersonation, AD kerberoasting, credential harvesting, and tool recommendations for red teamers.

This comprehensive skill helps establish and maintain a foundational 'Product Marketing Context' document. It serves as a single source of truth, capturing critical details like Ideal Customer Profile (ICP), core pain points, competitive landscape, and brand voice. Use this skill at the start of any new project to ensure consistent, deep, and accurate marketing messaging across all downstream tasks.

A comprehensive toolkit designed for Product Heads to define, cascade, and align Objectives and Key Results (OKRs) across the entire organization. This tool supports quarterly strategic planning, allowing users to define company-wide goals, decompose them into product objectives, and further cascade them to specific team goals. It measures critical alignment scores (vertical, horizontal) and generates structured roadmaps for maximizing organizational excellence and strategic focus.

This skill performs deep, line-by-line scanning of an entire codebase to understand its architecture, patterns, and dependencies. It systematically identifies critical issues across security vulnerabilities (SQL injection, secrets), performance bottlenecks (N+1 queries, caching), architectural flaws, and code quality gaps. It then automatically refactors and fixes all identified issues, transforming the code into professional, production-grade, enterprise-level quality.

Blueprint for building SEO-optimized pages at scale using data-driven templates, focused on unique value, clean architecture, indexing, and quality governance. Ideal for teams creating directory, comparison, or location landing pages.

This skill acts as an expert architect to generate complete, production-ready Python project structures. It supports various modern architectures, including FastAPI, Django (full-stack), standalone libraries, and CLI tools. It ensures adherence to best practices, includes dependency management (uv, pip), type hinting, and comprehensive testing skeletons.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.