Results for “knowledge-graph-reasoning”

Build structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.

Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".

Knowledge graph and smart memory management using graphify + Obsidian-inspired patterns. Use when: setting up a knowledge graph, managing memory health, cross-linking notes, compiling wiki pages from scattered notes, adding structured frontmatter, or running memory health checks. Triggers on: 'knowledge graph', 'graphify', 'wiki', 'cross-link', 'memory health', 'frontmatter', 'compile notes', 'wikilinks'.

Knowledge-graph-augmented retrieval. Entity and triple extraction, graph construction (Neo4j, LlamaIndex PropertyGraphIndex), hierarchical community summarization (Microsoft GraphRAG), personalized PageRank (HippoRAG), multi-hop traversal retrieval, and hybrid graph + vector pipelines. USE WHEN: user mentions "GraphRAG", "HippoRAG", "knowledge graph RAG", "entity extraction", "multi-hop reasoning", "Neo4j RAG", "LlamaIndex property graph", "LangChain graph retriever", "triple extraction", "community summarization" DO NOT USE FOR: vanilla vector RAG - use `rag-patterns`; multimodal inputs - use `multimodal-rag`; production indexing ops - use `rag-production`; hallucination checks - use `rag-guardrails`

Knowledge Craft

Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

Build, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".

Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.

Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships

any input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report. Use when user asks any question about a codebase, project content, architecture, or file relationships — especially if graphify-out/ exists. Provides persistent graph with god nodes, community detection, and BFS/DFS query tools.

Maximally Endowed Graph Architecture — λ-calculus over bounded n-SuperHyperGraphs with grounded uncertainty, conditional self-duality, and autopoietic refinement. Use when (1) simple graphs insufficient (η<2), (2) multi-scale reasoning required, (3) uncertainty is structured not stochastic, (4) knowledge must self-refactor. Pareto-governed: complexity added only when simpler structures fail validation.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Reads existing per-leader research files to map co-authorships, endorsements, and organizational ties across movement leaders, producing a cross-leader network graph with clusters, bridges, and isolates.

Ranks mutual connections by bridge value for warm introductions, using a weighted graph model with decay and engagement bonuses.

DriveLM: Driving with Graph Visual Question Answering

Temporal interval algebra for reasoning about time relationships in planning and knowledge representation

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Use when extracting entities and relationships, building ontologies, compressing large graphs, or analyzing knowledge structures - provides structural equivalence-based compression achieving 57-95% size reduction, k-bisimulation summarization, categorical quotient constructions, and metagraph hierarchical modeling with scale-invariant properties. Supports recursive refinement through graph topology metrics including |R|/|E| ratios and automorphism analysis.

Builds a navigable knowledge graph from any folder of files, with community detection, an audit trail, and outputs including interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Turns any folder of files into a navigable knowledge graph with community detection, producing interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.

Composite skill — query, capture, improve, and persist knowledge in one workflow. Chains recall (RAG query) → sync-memories (write durable note) → rag-curate (improve weak retrievals) → handoff (durable snapshot if session-ending). Use when the work involves "what did we decide", "remember this", "save where we are", or any closing checkpoint.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Build and maintain a persistent, interlinked markdown knowledge base that compiles knowledge once and keeps it current, with cross-references and contradiction flags.

当需要将个人知识或经验转化为商业机会，或希望建立有意义的个人发展路径时

Codebase Memory — Knowledge Graph Tools

Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.

Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.