Loading...
Loading...
Found 57 Skills
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.
Use when working with context management context save
Design short-term, long-term, and graph-based memory architectures
Search and analyze AI coding assistant session history using Terraphim. Find past conversations, discover patterns, and learn from previous work. Supports Claude Code, Cursor, Aider, and other AI coding assistants.
Use when implementing agent memory, persisting state across sessions, building knowledge graphs, tracking entities, or asking about "agent memory", "knowledge graph", "entity memory", "vector stores", "temporal knowledge", "cross-session persistence"
Complete GRACE methodology reference. Use when explaining GRACE to users, onboarding new projects, or when you need to understand the GRACE framework — its principles, semantic markup, knowledge graphs, contracts, and unique tag conventions.
Knowledge graph-based text replacement using Terraphim hooks. Intercepts commands and text to apply transformations defined in the knowledge graph. Works with Claude Code PreToolUse hooks and Git prepare-commit-msg hooks.
High-performance code intelligence MCP server that indexes codebases into knowledge graphs for structural queries, call traces, and architecture analysis
Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.
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.