code-review-graph
SkillDev tools"Use code-review-graph for local-first CLI/MCP code knowledge
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the code-review-graph skill
What this skill tells your AI
The instructions your AI receives, as published by vectorspacelab/arex-skill in skills/repositories/repo-skills/code-review-graph/SKILL.md and read by ahel’s review.
Use this repo skill when a task names code-review-graph, CRG, graph-backed code review, local MCP graph tools, blast-radius analysis, structural code search, risk-scored PR review, custom language parsing, or multi-repo code graph operations.
code-review-graph is a Python package and CLI/MCP server that builds a local SQLite code knowledge graph from Tree-sitter parses, updates it incrementally, and exposes token-efficient review/search workflows to coding agents.
Fast route
- For first-time setup, read references/start-here.md, then route to
install-and-setup. - For changed-code review or PR review, route to
review-changes. - For graph queries, architecture, flows, communities, or refactors, route to
graph-exploration. - For optional custom languages, embeddings, wiki, registry/daemon, GitHub Action, or eval, route to
integrations-and-extensions.
Minimal install and smoke check
pip install code-review-graph
code-review-graph install
code-review-graph build
code-review-graph status
For an existing install, the bundled scripts/check_crg_install.py verifies import, package version, CLI discovery, and packaged docs without creating or updating a graph.
Sub-skill routes
| Sub-skill | Read when |
|---|---|
| install-and-setup | Installing CRG, configuring MCP clients, building/updating the graph, checking status, running the MCP server, visualizing, watching, or uninstalling. |
| review-changes | Reviewing a diff or PR, computing blast radius, risk scoring, test gaps, token savings, affected flows, or rendering a PR review comment. |
| graph-exploration | Finding callers/callees/tests/imports, searching graph nodes, inspecting flows/communities/architecture, finding large functions, or previewing refactors. |
| integrations-and-extensions | Custom languages, embeddings/providers, wiki generation, multi-repo registry, daemon watch workflows, GitHub Action setup, or eval/benchmark reproduction. |
Shared references
- references/cli-reference.md maps common CLI commands and flags.
- references/mcp-tools.md maps MCP tools and token-discipline rules.
- references/troubleshooting.md covers cross-cutting install, graph freshness, optional dependency, provider, and SQLite issues.
- references/architecture-and-schema.md summarizes graph node/edge semantics.
- references/repo-provenance.md records the source snapshot used to create this skill and when to refresh it.
- references/repo-routing-metadata.json is structured metadata for managed repo-skill routing.
Operating principles
- Prefer graph tools over broad file reads when the user asks a structural question.
- Start with compact context (
get_minimal_context_tool) when an MCP session is available. - Use
detail_level="minimal"unless the next step requires source snippets or full graph details. - Keep optional extras optional; do not install embeddings, communities, wiki, eval, enrichment, or
allunless the task needs them. - Treat cloud embeddings and external services as opt-in because code-derived text may leave the machine.
- Rebuild or update the graph before trusting review/search answers after a branch switch, rebase, or large edit.
Boundaries and non-fits
Do not use this skill as a general-purpose LSP replacement, full source-code reading strategy, or benchmark runner unless the user explicitly asks for CRG workflows. This skill covers the Python CLI/MCP package and public integrations, not detailed development of the separate VS Code extension package.
Verification expectations
This runtime skill is grounded by package metadata, public docs, installed-package inspection, existing repo-local CRG skills, and native test candidates spanning install, CLI, MCP, review, search, flows, communities, refactor, optional integrations, and CI workflow safety. Final verification artifacts live outside this runtime skill under the review/test artifact directory.
Signals
- GitHub stars
- 266
- Forks
- 21
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packagesK1binfo
installs-packages (in references/start-here.md)K1binfo
installs-packages (in references/troubleshooting.md)K1binfo
installs-packages (in sub-skills/install-and-setup/SKILL.md)K1binfo
installs-packages (in sub-skills/install-and-setup/references/troubleshooting.md)K1binfo
installs-packages (in sub-skills/install-and-setup/references/workflows.md)K1binfo
installs-packages (in sub-skills/integrations-and-extensions/references/embeddings.md)
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Catalog kind
- skill
- Gateway key
code-review-graph-vectorspacelab- Source
- github.com/vectorspacelab/arex-skill