Maintainer of this skill?
Automated security scan — not a certification.
by tirth8205
Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
# Add to your Claude Code skills
git clone https://github.com/tirth8205/code-review-graphGuides for using mcp servers skills like code-review-graph.
Last scanned: 4/17/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-04-17T06:07:13.106Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}See how code-review-graph compares with popular alternatives.
code-review-graph is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by tirth8205. Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows. It has 32,447 GitHub stars.
Yes. code-review-graph passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/tirth8205/code-review-graph" and add it to your Claude Code skills directory (see the Installation section above).
code-review-graph is primarily written in Python. It is open-source under tirth8205 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh code-review-graph against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
AI coding tools often re-read large parts of a codebase to review a change. code-review-graph builds a structural map of the code with Tree-sitter, keeps it updated incrementally, and serves compact context over MCP, so the assistant reads only the files a change touches.
pip install code-review-graph # or: pipx install code-review-graph
code-review-graph install # detect installed AI coding tools and configure each one
code-review-graph build # parse the codebase
install detects which AI coding tools you have, writes an MCP server entry for each, installs hooks and skills where the platform supports them, and adds graph instructions to the platform's rules file. The MCP entry uses poetry run or uv run inside a Poetry or uv project environment, uvx code-review-graph serve when uvx is on PATH, and otherwise the current Python interpreter. Restart the editor or tool afterwards.
To configure one platform, pass --platform with one of codex, claude-code, cursor, windsurf, zed, continue, opencode, antigravity, gemini-cli, qwen, kiro, qoder, copilot, copilot-cli, codebuddy, or hermes:
code-review-graph install --platform cursor
code-review-graph install --platform codebuddy
Config file locations are listed in docs/USAGE.md. Requires Python 3.10+.
uninstall removes CRG-owned files and entries from a Git or SVN working tree and leaves other MCP servers, hooks, skills and JSONC comments alone. Run it from anywhere inside the tree. Shared config files are replaced atomically, so a failed write leaves the original intact.
code-review-graph uninstall --dry-run # preview only
code-review-graph uninstall # preview, confirm, apply
code-review-graph uninstall --yes # apply without prompting
code-review-graph uninstall --all-repos # also clean every registered repository
code-review-graph uninstall --keep-data # remove integrations, keep graph databases
code-review-graph uninstall --keep-user-configs --repo . # this project only
Then open the project and ask the assistant:
Build the code review graph for this project
Build time scales with repository size; a cold build of a ~3,000-file repository took about 40 seconds (measured). After that, hooks and watch mode keep the graph updated. If some files fail to parse, the result has status partial and names them in its summary; the CLI also prints a Warning: line on stderr, and those files keep their previous graph rows.
The repository is parsed into ASTs with Tree-sitter and stored as a graph of nodes (functions, classes, imports) and edges (calls, inheritance, test coverage). At review time the graph is queried for the smallest set of files the assistant needs to read.
When a file changes, the graph traces every caller, dependent and test that could be affected. The assistant reads those files instead of scanning the whole project.
Hooks, the pre-commit hook and watch mode trigger incremental updates. The update diffs changed files, finds their dependents through the graph's import and call edges, and re-parses only the files whose SHA-256 hash changed. On a ~3,000-file project (django) a two-file edit re-indexes in about 2.5 seconds on the path the hooks use, of which ~1.4 s is process start-up; a no-op update costs only that start-up. See Incremental update latency.
Instead of feeding a whole corpus to the model, the graph returns a slice shaped to the question. In the 2026-08-02 capture of this repository at 84bde354, 208,821 source tokens became ~3,190 tokens per question. The repository has grown a lot since that snapshot, so both numbers are larger today.
The parser extracts functions, classes, imports, call sites, inheritance and tests, using Tree-sitter where a grammar exists and targeted fallbacks elsewhere. Supported: Python, JavaScript/TypeScript/TSX, Go, Rust, Java, C/C++, C#, VB.NET, Ruby, Kotlin, Swift, PHP, Scala, Solidity, Dart, R, Perl, Lua/Luau, Objective-C, shell scripts, Elixir, Zig, PowerShell, Julia, ReScript, GDScript, Nix, Verilog/SystemVerilog, SQL, Terraform/OpenTofu (.tf; other .hcl files become file nodes only), Ansible YAML (playbooks, roles, tasks), Spring Boot application config (application.properties, application.yml, application.yaml and their application-<profile> variants; key names and value types only, never values), Vue/Svelte SFCs, Astro files (parsed with the TypeScript grammar), Jupyter and Databricks notebooks (.ipynb), and Perl XS files (.xs). Other YAML and other .properties files are not treated as source code.
PHP projects also get repository-bounded Composer PSR-4 resolution, Blade template references, and Laravel Route and Eloquent edges when the source shows explicit framework imports, model inheritance and receiver evidence.
Java projects get Spring dependency-injection call resolution, request endpoints and WebFlux routes, scheduled triggers, application-event publis