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データ・研究 · SKILL.md

code-review-graph

code-review-graph

Use code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: code-review-graph description: "Use code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations." disable-model-invocation: true metadata: disco-role: operating license: MIT

code-review-graph

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

  1. For first-time setup, read references/start-here.md, then route to install-and-setup.
  2. For changed-code review or PR review, route to review-changes.
  3. For graph queries, architecture, flows, communities, or refactors, route to graph-exploration.
  4. 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-skillRead when
install-and-setupInstalling CRG, configuring MCP clients, building/updating the graph, checking status, running the MCP server, visualizing, watching, or uninstalling.
review-changesReviewing a diff or PR, computing blast radius, risk scoring, test gaps, token savings, affected flows, or rendering a PR review comment.
graph-explorationFinding callers/callees/tests/imports, searching graph nodes, inspecting flows/communities/architecture, finding large functions, or previewing refactors.
integrations-and-extensionsCustom languages, embeddings/providers, wiki generation, multi-repo registry, daemon watch workflows, GitHub Action setup, or eval/benchmark reproduction.

Shared references

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 all unless 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.

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