Retrieve ranked context from local knowledge graphs and memory files.
npx skills add https://github.com/0xUrsanomics/utopia-os --skill recall-0xursanomics
Please help me install this Agent Skill. Skill: recall Source: https://github.com/0xUrsanomics/utopia-os/tree/main/skills/recall Command: npx skills add https://github.com/0xUrsanomics/utopia-os --skill recall-0xursanomics
This skill solves the problem of fragmented information by providing a unified, brain-first search interface that retrieves context from your personal notes, memory files, and past work without relying on external web searches.
Use the recall skill to search your memory for any notes or project status updates regarding the current project.
High-intent search queries and answers about installing and using this skill.
Semantic search across local memory files is performed by combining BM25 keyword matching with vector embeddings to retrieve and rank relevant context from your personal knowledge graph without relying on external web searches.
Yes, you can use local sentence-transformer models and a pre-indexed LanceDB store to execute hybrid search and reranking, which automatically surfaces prior decisions and project history to ground AI responses.
The best way to recall past decisions is using a hybrid retrieval approach that scans your local knowledge graph and memory files, combining vector-based semantic understanding with keyword matching to provide accurate, ranked results.
Yes, hybrid vector search requires a pre-indexed LanceDB store along with local sentence-transformer models to operate, enabling the system to execute ranked, context-aware information retrieval for complex queries.
Semantic search returns results despite differing keywords because it combines vector-based semantic understanding with BM25 keyword matching, allowing it to find relevant information even when the exact search terms do not match.
A key limitation is the requirement for a pre-indexed LanceDB store and local sentence-transformer models; without this environment setup, the hybrid search and reranking protocols cannot execute to retrieve context.
AlexiosBluffMaraStore and query embeddings with metadata filtering in a local vector database.
L-LesterYuSearches the web via the Brave Search API and returns ranked results or AI-generated summaries.
OriNachumManage AI agent memory with notes, MongoDB RAG, and Neo4j graphs.
JuanMS20Index Markdown and text files for local hybrid search with BM25, vectors, and reranking.
