Query existing LightRAG knowledge for retrieval-backed answers without ingesting documents.
npx skills add https://github.com/spoonbobo/trinity --skill query-knowledge
Please help me install this Agent Skill. Skill: query-knowledge Source: https://github.com/spoonbobo/trinity/tree/main/app/skills/query-knowledge Command: npx skills add https://github.com/spoonbobo/trinity --skill query-knowledge
This Skill allows users to retrieve answers and insights from existing indexed knowledge without the need to upload, ingest, or modify documents, streamlining information retrieval.
Use the query-knowledge skill to search for 'authentication methods' within the active workspace.
High-intent search queries and answers about installing and using this skill.
You can query indexed LightRAG documents without re-uploading by using a workspace-scoped retrieval skill to search and retrieve answers from pre-existing knowledge bases without ingesting or mutating documents.
Yes, you can inspect document status within your workspace to check the current indexing status and retrieve specific chunk details of already indexed documents.
Comparing indexed documents based on a specific query allows you to analyze differences between existing sources, returning targeted retrieval-backed insights without modifying the original files.
No, knowledge retrieval operates strictly on scoped retrieval queries and document inspections, adhering to workspace contracts without requiring any document ingestion or mutation.
Retrieving evidence from indexed LightRAG sources involves querying your scoped workspace to extract supporting information and insights directly from the pre-existing knowledge base.
spoonboboIngests, indexes, and queries documents in a claw-scoped knowledge workspace via LightRAG.
sirius-zuoExtract key facts, named entities, and dates from raw text.
dyzsasdRegisters a new virtual project in a dev-loop workspace and syncs metadata with backend systems.
mupengi-botDesign RAG systems with embedding models, vector databases, and retrieval optimization.
