Build graph-based retrieval workflows with GraphRAG
Index private text into graph-backed retrieval structures, tune prompts, and query the resulting knowledge graph for RAG workflows.
npx skills add agentskillexchange/skills --skill build-graph-based-retrieval-workflows-with-graphrag
Use GraphRAG when private or narrative text needs graph-based retrieval before an agent can reason over it. The operator initializes a GraphRAG project, indexes a bounded corpus, reviews indexing cost and configuration, tunes prompts when needed, and queries the graph-backed retrieval layer for downstream agent or RAG answers. Invoke this instead of normal library use when the task is to transform unstructured private text into a graph memory structure for retrieval. The boundary is graph-based indexing and query over a controlled corpus; do not present GraphRAG as a generic RAG framework.
What this skill actually does
Inputs and prerequisites: Python, GraphRAG CLI/package, LLM credentials, bounded text corpus.
Setup notes: Follow the GraphRAG command-line quickstart, initialize a project with graphrag init –root [path], add a small bounded corpus, review configuration and cost guidance, then run indexing and query commands before connecting results to an agent or RAG workflow.
Source and verification boundary: use https://microsoft.github.io/graphrag/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.