Skill UI

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This guide provides a deep dive into the cryptographic techniques used by ransomware, including AES, RSA, ChaCha20, and hybrid schemes. It covers identifying algorithms, analyzing key management patterns (strong, weak, flawed), and reverse engineering file encryption routines to assess decryption feasibility and support recovery efforts.

This skill enables comprehensive security graph mapping by ingesting both on-premises Active Directory data (via SharpHound) and cloud Entra ID data (via AzureHound) into BloodHound CE. It analyzes complex, hybrid attack paths, allowing security teams to identify critical privilege escalation chains from low-privileged accounts to Tier-0 assets, mapping findings to MITRE ATT&CK.

This skill guides the secure migration from classical cryptography (RSA, ECC) to quantum-resistant algorithms (PQC). It covers building a Cryptographic Bill of Materials (CBOM), identifying data at risk (HNDL), and deploying hybrid key exchange protocols like X25519MLKEM768 to ensure long-term data security against quantum threats.

A comprehensive skill for interacting with Weaviate vector databases. It enables advanced operations including semantic search, hybrid keyword querying, schema inspection, data exploration, collection creation, and bulk data import (CSV, JSON, PDF). Use this skill when you need to query data conceptually, retrieve specific raw objects, or manage the lifecycle of Weaviate collections.

This skill provides a hybrid, persistent, and searchable knowledge memory system specifically designed for enhancing AI agents' capabilities. It allows developers to record, retrieve, and search key knowledge, architectural decisions, and operational memories automatically. Use it to build agents with long-term context and reliable knowledge management, ensuring findings are synced back into project documentation.

A state-of-the-art memory search engine providing multi-faceted retrieval strategies. It supports default dense semantic matching, advanced hybrid search (sparse + dense with RRF), deep multi-hop Graph RAG for complex reasoning, and 'Smart' search utilizing query expansion, recency boosting, and MMR diversity reranking. Ideal for retrieving nuanced, diverse, and time-sensitive information from large, complex knowledge bases across multiple namespaces.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.