Index Markdown directories into an in-memory knowledge graph for semantic queries.
npx skills add https://github.com/cameronsjo/dev-toolkit --skill gibram
Please help me install this Agent Skill. Skill: gibram Source: https://github.com/cameronsjo/dev-toolkit/tree/main/skills/gibram Command: npx skills add https://github.com/cameronsjo/dev-toolkit --skill gibram
GibRAM provides an in-memory knowledge graph and vector search to organize and explore large collections of Markdown documents locally.
Index a directory of Markdown files into GibRAM and start querying for related concepts.
High-intent search queries and answers about installing and using this skill.
You can build a local knowledge graph from markdown notes by indexing a directory of files into an in-memory graph. This extracts entity nodes and relationships, enabling fast, private semantic queries across your document corpus.
Yes, you can use Ollama for local vector search across ADRs. The system supports configurable embedders, allowing you to choose local Ollama or OpenAI backends to generate embeddings for your markdown documents.
Tracing concept relationships across specs requires indexing them into a session-scoped knowledge graph. This enables cross-document reasoning by mapping entity nodes and their connections for semantic exploration.
No, this markdown indexing tool does not require an external database server. It uses a local in-memory knowledge graph to store entity nodes, relationships, and embeddings for your research session.
Querying a markdown knowledge graph for related decisions involves using semantic search across the indexed corpus. After indexing your ADRs and notes, you can search for concepts to trace prior decisions and relationships.
Yes, you can integrate this markdown knowledge graph with Claude for document research. It includes MCP integration to support querying and indexing workflows within your AI-assisted research sessions.
jugrajsinghManage embedding provider and model configurations for the grepai tool.
GodSpeedAIQuery SEA-Forge's Knowledge Graph via SPARQL to retrieve concepts and relationships.
rhpdsConfigure persistent Memory MCP knowledge graph storage for Claude Code sessions.
CarlosAndresBF1Build RAG pipelines with vector stores and retrieval strategies for document Q&A.
