Skill UI

A persistent knowledge base that transcends standard RAG by integrating three core layers: raw source storage, a structured knowledge graph, and a dynamic wiki layer. It ingests documents once, automatically extracting entities and relations to build a comprehensive, cross-referenced, and verifiable knowledge graph. Ideal for enterprise research, complex Q&A, and deep knowledge accumulation.

This skill extracts full transcripts and rich metadata from a single YouTube video URL. It uses yt-dlp to download subtitles, cleans VTT timing markers, and pulls video details (title, channel, date). The output is a structured, queryable Markdown file, ideal for populating a personal knowledge vault and enabling downstream knowledge graph extraction or content repurposing.

An expert AI specializing in Slang shader programming for high-performance graphics and compute applications. This model excels at writing, reviewing, refactoring, and optimizing production-grade code for various graphics stages (vertex, fragment, compute, ray tracing, tessellation). It ensures cross-platform compatibility across major APIs (D3D12, Vulkan, Metal, OpenGL) and supports advanced concepts like generics, parameter blocks, and automatic differentiation. Use this skill when integrating shaders into C++ engines or requiring deep knowledge of low-level GPU pipelines.

This guide details how to deploy OpenCTI, a powerful, open-source threat intelligence platform, and utilize the pycti Python client. It teaches analysts how to model complex cyber threat intelligence, including threat actors, campaigns, and indicators, into a structured STIX 2.1 knowledge graph. Use this for centralized threat-intel correlation, building adversary graphs, and generating STIX bundles for detection engineering.

A sophisticated, dark-themed presentation template designed for AI-native projects, knowledge graphs, or deep technical deep dives. It features a gradient cover with floating orbs and an interactive SVG force-directed graph, complemented by JetBrains Mono code highlighting and modern glassmorphism elements for section slides. Perfect for tech talks and product launches.

Automates the creation of Architecture Decision Records (ADRs). This tool manages the entire lifecycle of an ADR: determining the next sequential ID, generating a standardized Markdown file, storing the record in the AgentDB graph, and automatically linking it to related decisions. Essential for documenting significant architectural choices, API designs, and data model changes to maintain system knowledge coherence.

Automatically indexes all Architecture Decision Records (ADRs) within a monorepo. It parses both v3-style and plugin-style ADR formats, persisting the content into the `adr-patterns` namespace and all relationships (supersedes, amends, related, depends-on) into the `adr-edges` namespace. This is crucial for maintaining a single source of truth for architectural history and dependencies, significantly improving knowledge retrieval and graph integrity.

A comprehensive tool for querying, storing, and managing complex knowledge across multiple agent sessions. AgentDB utilizes advanced techniques including semantic routing, hierarchical recall, causal graph construction, and context synthesis. Use this skill when your application requires robust, multi-faceted knowledge retrieval and reasoning based on structured and unstructured data. It provides a structured workflow for checking system health, starting sessions, storing knowledge in tiers (working, episodic, semantic), and searching learned patterns.

This tool performs a deep, recursive, and parallel investigation, building a comprehensive graph-structured dossier around a specified seed entity. It expands outward across diverse sources, including web search, internal memory stores, codebases, and knowledge graphs. It captures all connected entities and the provenance of every claim, making it ideal for mapping the full context or relationship network of a subject.

This tool is designed to analyze source code by extracting core entities (classes, functions, modules, types) and mapping their complex relationships (imports, inheritance, calls, dependencies). It constructs a comprehensive Knowledge Graph (KG) in AgentDB. This is essential for detailed dependency analysis, understanding large codebase structures, and performing accurate impact assessments.

This tool performs pathfinder traversal across a knowledge graph starting from a specified seed entity. It expands outward through causal edges, calculates path relevance scores, and prunes low-similarity branches. It is ideal for conducting impact analyses, tracing dependency chains, or understanding complex relationships within a structured knowledge base.

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.