Skill UI

Guides systematic detection and exploitation of cross-site scripting and HTML injection flaws, covering stored/reflected/DOM vectors, payload crafting, and remediation advice for web security assessments.

This tool optimizes the codebase indexing within Cursor. It processes your entire codebase, converting code chunks into vector embeddings stored in a vector database. This enables advanced @Codebase semantic search, allowing users to ask natural language questions (e.g., "Where is authentication handled?") and retrieve the most relevant code snippets, significantly improving AI context awareness across chat and coding tasks.

A comprehensive skill for creating, editing, and automating vector graphics and technical diagrams using LibreOffice Draw. It supports the native ODG format and facilitates seamless format conversion between ODG, SVG, PDF, PNG, and JPG. Ideal for generating flowcharts, organizational charts, and technical schematics, and for batch processing graphics operations via command-line or Python UNO scripting.

A comprehensive guide for selecting and optimizing embedding models for advanced vector search and retrieval-augmented generation (RAG) applications. This guide covers model comparison, chunking strategies, dimension reduction techniques, and provides runnable Python templates for both OpenAI and local Sentence Transformer implementations.

FAISS (Facebook AI Similarity Search) is a library designed for efficient similarity search and clustering of dense vectors. It supports indexing and searching on billions of vectors at scale, featuring GPU acceleration and multiple index types (Flat, IVF, HNSW, PQ) to balance speed and accuracy. It is essential for building high-performance, large-scale applications requiring fast k-NN retrieval or pure vector similarity matching, especially in fields like RAG and recommendation systems.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

LangChain accelerates building LLM-powered agents, RAG pipelines, and chatbots by orchestrating models, chains, tools, memory, and vector stores across major providers for rapid prototyping or production deployment.

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.

LlamaIndex is a data-centric framework for building LLM applications, offering 300+ connectors, vector indices, query engines, agents, and multimodal support to power document Q&A, chatbots, knowledge retrieval, and RAG pipelines over private data.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.