From graqle
Set up GraQle on a repository — scan it into a knowledge graph, verify graph health, and run the first graph-powered question. Use when GraQle is installed but no graqle.json exists yet, or when the user asks to onboard, scan, or index the repo with GraQle.
How this skill is triggered — by the user, by Claude, or both
Slash command
/graqle:graph-onboardThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Take a repository from zero to a queryable knowledge graph.
Take a repository from zero to a queryable knowledge graph.
graq doctor in the shell. If graq is not found,
install with pip install graqle first.graq scan repo . from the repository root. This builds graqle.json
(nodes for modules, functions, classes, endpoints and their dependency edges).
The first full scan is never blocked by licensing caps, whatever the repo
size (scan time itself scales with the repo).graqle.yaml controls which LLM provider powers
reasoning (Anthropic by default; bring your own key via environment variables).graq_inspect for node/edge counts and hub nodes, and
graq_graph_health for embedding/cache status. If chunks are unembedded,
follow its recommendation before relying on semantic recall.graq_reason(question="What are the highest-risk modules in this repo and why?") to demonstrate graph-powered reasoning.graq_learn after completing tasks, and let
graq init install git hooks so the graph stays in sync with the code.npx claudepluginhub quantamixsol/graqle --plugin graqleBuilds or incrementally updates the repository code knowledge graph for review. Use for initial setup, after refactors or branch switches; supports Python, TypeScript/JS, Go; SQLite storage.
Builds a local knowledge graph of code, docs, and mixed media using tree-sitter extraction and LLM-based relationship inference. Query via CLI commands instead of reading raw files.
Uses codebase knowledge graphs for architecture-aware task decomposition, dependency discovery, and context reduction during planning.