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Find the right GitNexus tools and workflows for exploring code relationships. This reference explains available commands, graph queries, and resources, including how to check which branch an answer describes and whether its index is current.
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Explore an unfamiliar codebase using a graph of code relationships. Inspect its structure, find functions and classes, trace calls and imports, follow execution paths, and locate large functions for closer investigation.
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Review all changes in a pull request or branch against its base branch. Use a knowledge graph, a map of code relationships, to report affected files and functions, test coverage, API compatibility issues, and recommended actions.
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Routes requests about a codebase to the appropriate skill for mapping its structure, generating knowledge graphs, updating documentation, extracting lessons, or storing and recalling project knowledge for future planning.
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Investigate a topic against preserved sources and write a draft-status research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.
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When the user wants to optimize for entity recognition, Knowledge Graph, or entity-based SEO. Also use when the user mentions "entity SEO," "entity optimization," "Knowledge Graph," "Knowledge Panel," "entity signals," "brand entity," "entity linking," "entity relationships," or "entity-first content." For structured data, use schema-markup.
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Knowledge graph integration for token-efficient codebase understanding. Uses codebase-memory MCP for AST indexing, dependency graphs, and smart context selection. 6-71x token savings vs raw file reading.
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Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
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Use code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.
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Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction workflows.
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Use when analyzing vault structure, finding orphan notes, discovering missing connections, identifying bridge concepts, or checking vault health from a graph perspective. Triggers on "vault graph", "map vault", "find orphans", "missing links", "vault structure", "knowledge graph".
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Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.
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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.
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Manage knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.
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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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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.
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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manage knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.
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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.
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코드의 함수와 호출 관계, 실행 흐름을 연결된 도표로 만들어 브라우저에서 탐색할 수 있게 합니다. Cloudflare를 통한 원격 공유도 지원합니다.
API로 SiYuan 노트를 검색하고 읽으며 편집합니다. 문서 생성, 문단 추가·수정, 이름 변경과 삭제를 처리하고 Markdown으로 내보낼 수 있습니다.
Create an onboarding guide for new project members from a knowledge graph, a map of project components. The Markdown guide covers architecture, key concepts, a suggested learning path, important files, and areas that need extra care.
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Get a detailed explanation of a file, function, or module using an existing knowledge graph and its source code. It explains architectural roles, dependencies, callers, and data flow for readers who may not know the programming language.
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Investigate a username, file, code identifier, URL, or concept by exploring connected entities across relevant sources. Produce a dossier with a summary, relationship diagram, source references, and a machine-readable JSON graph.
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