Loading...
Loading...
Found 44 Skills
Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships
Trace bugs through call chains using knowledge graph
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?"
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.
Advanced RAG with Self-RAG, Corrective-RAG, and knowledge graphs. Use when building agentic RAG pipelines, adaptive retrieval, or query rewriting.
Use when saving or retrieving important patterns, decisions, and learnings across sessions. Triggers on keywords like "remember", "save pattern", "recall", "memory", "persist", "knowledge base", "learnings".
Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system boundaries and invariants, migrating between data models, establishing taxonomies or hierarchies, user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when scattered/inconsistent data structures need formalization.
Deep code analysis for pplx-sdk — parse Python AST, build dependency graphs, extract knowledge graphs, detect patterns, and generate actionable insights about code structure, complexity, and relationships. Use when analyzing code quality, mapping dependencies, or building understanding of the codebase.
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.
Neo4j graph database with Cypher query language. Use for graph-based data.
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.
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".