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Found 97 Skills
Implement GraphRAG patterns combining knowledge graphs with retrieval for complex reasoning. Use this skill when building RAG over interconnected data or needing relationship-aware retrieval. Activate when: GraphRAG, knowledge graph, graph retrieval, entity relationships, Neo4j RAG, graph database, connected data.
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
Automatically find relevant context from knowledge graph and code relationships while coding. Detects when context would be helpful (new files, unfamiliar code, architectural decisions) and surfaces related entities, prior decisions, and code dependencies.
CLI for Limitless.ai Pendant with lifelog management, FalkorDBLite semantic graph, vector embeddings, and DAG pipelines. Use for personal memory queries, semantic search across lifelogs/chats/persons/topics, entity extraction, and knowledge graph operations. Triggers include "lifelog", "pendant", "limitless", "personal memory", "semantic search", "graph query", "extraction".
Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.
Comprehensive skill for Graphiti and Zep - temporal knowledge graph framework for AI agents with dynamic context engineering
Task management via Basic Memory schemas: create, track, and resume structured tasks that survive context compaction. Uses BM's schema system for uniform notes queryable through the knowledge graph.
Analyze a complete literary work into a structured Basic Memory knowledge graph. Covers schema design, entity seeding, chapter-by-chapter processing, cross-referencing, validation, and visualization.
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."
Maintains persistent codebase knowledge across sessions through a structured knowledge graph stored in a local Obsidian vault (.doctrack/). Use this skill whenever you have just made meaningful code changes (new features, modified components, refactoring, bug fixes) to update the project's documentation. Also use it when the user asks to document code, update docs, sync documentation, initialize documentation for an existing project, or when you want to understand the existing codebase structure at the start of a session. This skill should be used proactively after any significant code modification — don't wait for the user to ask. If you changed code, update the docs. Think of it as your long-term memory system: read before working, write after changing. Also use this when a user says "doctrack init", "doctrack refresh", "refresh docs", "update docs", "sync docs", "initialize docs", "document this project", or wants to bootstrap documentation for a codebase that has no .doctrack/ vault yet.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory, or memory benchmarks (LoCoMo, LongMemEval). A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of durable agent knowledge and cross-session persistence.
Search the code knowledge graph by function, class, or type using FTS5 full-text search.