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Found 97 Skills
Show the current health status of a GRACE project. Use to get an overview of project artifacts, codebase metrics, knowledge graph health, and suggested next actions — helps identify drift, missing contracts, or unpaired semantic blocks.
Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
Builds and queries code knowledge graph for dependency analysis, references, implementations, and architecture overview. Use when starting work on unfamiliar codebase or before refactoring.
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.
Stores decisions and patterns in knowledge graph. Use when saving patterns, remembering outcomes, or recording decisions.
Trace bugs through call chains using knowledge graph
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.
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
Generates hierarchical knowledge graphs via Recursive Pareto Principle for optimised schema construction. Produces four-level structures (L0 meta-graph through L3 detail-graph) where each level contains 80% fewer nodes while grounding 80% of its derivative, achieving 51% coverage from 0.8% of nodes via Pareto³ compression. Use when creating domain ontologies or knowledge architectures requiring: (1) Atomic first principles with emergent composites, (2) Pareto-optimised information density, (3) Small-world topology with validated node ratios (L1:L2 2-3:1), or (4) Bidirectional construction. Integrates with graph (η≥4 validation), abduct (refactoring), mega (SuperHyperGraphs), infranodus (gap detection). Triggers: 'schema generation', 'ontology creation', 'Pareto hierarchy', 'recursive graph', 'first principles decomposition'.
Interact with the SlipBox semantic knowledge engine and read notes from PrivateBox. Use when capturing ideas, searching notes, browsing your knowledge graph, or running semantic analysis passes (link, cluster, tension).
Auto-Claude Graphiti memory system configuration and usage. Use when setting up memory persistence, configuring LLM/embedding providers, querying knowledge graph, or optimizing memory performance.
Use this skill when papers are collected in Zotero but the user wants detailed reading notes, project-linked literature synthesis, collection-wide paper-note coverage checks, and a connected knowledge map inside the bound Obsidian project knowledge base.