Extract structure
The code pipeline uses tree-sitter and specialized extractors to identify functions, classes, imports, calls, and other relationships. Parsing happens locally and does not require an LLM. How Graphify finds code dependencies walks through each step. Some formats use dedicated or heuristic parsers, so support differs by language and construct. SQL and Terraform have structural extractors too; their optional dependencies are listed in supported inputs.Add semantic context
Documents, papers, images, and transcripts can contribute concepts and relationships through a model. The skill uses the assistant environment;graphify extract supports configured headless backends.
This is a separate data path. A local code parse does not send files to a model, but semantic extraction may send its input to the provider you select. Local development explains the choices.
Resolve and merge
Graphify links references across files and combines fragments into one graph. An edge stores a relationship and a provenance label. Resolution can infer a connection from static evidence;INFERRED does not always mean an LLM created it.
Find communities
Community detection groups densely connected nodes into subsystems. Graphify uses Leiden when the relevant dependency is available and falls back to NetworkX Louvain. Clustering is based on graph structure. Community names can come from your assistant or a configured backend. Use--no-label with cluster-only when you want to avoid model-generated names.
Query a compact map
query gathers related context, path follows a connection, and explain inspects one node. MCP exposes the same graph through structured tool calls. These operations retrieve graph evidence; the assistant can then use it to form an answer.
Next steps
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Edges and provenance
How each relationship records where it came from.
Graph files and outputs
What a build writes and how to reuse it.