
Code Review Assistant
https://github.com/pbakaus/code-review-assistant- Category
- AI Agents
- Rank
- No. 2096Tools index
Previous survey · No. 2091 ·
- Listed in
- #4 Review any codebase
- Pricing
- Open Source
- Type
- AGENT
- Builder
- pbakaus
- GitHub
- 8 stars
- Date
About
Claude Code skill that provides AI-powered code review assistance for pull requests and diffs.
What it does
It gathers pull request metadata and changes, checks them against team expertise and coding standards, then recommends reviewers and flags concerns. It can also create architectural diagrams. Interactive runs ask before assigning people or posting comments, while non-interactive runs print results for CI.
Why it's ranked here
A strong choice for teams willing to encode their standards and ownership knowledge. It connects analysis to useful GitHub actions while keeping those actions behind confirmation. The verdict is tempered by several service credentials, incomplete handling of very large pull requests, and brittle reviewer parsing.
What's good
Generated files, lockfiles, build output, and coverage artifacts are excluded to reduce token use. Teams can customize expertise, standards, and output structure. The CLI accepts several common pull request identifier formats, supports unattended CI output, and separates recommendations from assignment and comment posting.
Tradeoffs
Basic operation requires Anthropic and GitHub credentials. Diagram generation adds Gemini and Cloudinary accounts. Pull request file retrieval requests only 100 files without pagination, so larger changes may be incomplete. Reviewer parsing only accepts word characters, which can lose hyphenated GitHub handles.
How to use it well
Use it on teams with documented ownership areas and recurring coding rules. Run it interactively for reviewer preparation or non-interactively as an advisory CI step. Keep human approval and automated tests alongside it. It is not a reliable exhaustive check for pull requests exceeding 100 changed files.
Technical notes+
The npm package in package.json is a TypeScript ESM application with the agent executable and a build driven by tsconfig.json. src/index.ts runs the Claude Agent SDK with the bundled skill, renders Markdown in the terminal, and prompts before GitHub mutations. src/github.ts uses Octokit for pull request data, limits file retrieval to per_page: 100, filters generated artifacts, and shells out to GitHub CLI for assignments and comments. .claude/skills/code-review-assistant/SKILL.md defines the standards and expertise workflow. Diagram and upload helpers live in .claude/skills/code-review-assistant/scripts/generate-diagram.js and .claude/skills/code-review-assistant/scripts/upload-image.js, with separate dependencies declared in .claude/skills/code-review-assistant/scripts/package.json.
Observed
- License
- Apache License 2.0
- Primary language
- TypeScript
- Packaging
- npm package using ECMAScript modules, with an agent executable
- Install surfaces
- npm install for the CLI, or copy the bundled skill into a Claude Code project
- Interfaces
- Interactive CLI, non-interactive CLI for CI, Claude Code skill, and Agent SDK usage
- External integrations
- Anthropic Agent SDK, GitHub Octokit and CLI, optional Gemini diagram generation, and optional Cloudinary uploads
- Tests
- No test directory or test script appears in the supplied repository tree
Read from README.md, package.json, src/index.ts, src/types.ts, src/github.ts, .claude/skills/code-review-assistant/scripts/upload-image.js, .claude/skills/code-review-assistant/scripts/generate-diagram.js, agent, .env.example, tsconfig.json, .claude/skills/code-review-assistant/SKILL.md, .claude/skills/code-review-assistant/LICENSE.txt, .claude/skills/code-review-assistant/scripts/setup.sh, .claude/skills/code-review-assistant/diagrams/README.md, .claude/skills/code-review-assistant/scripts/package.json.
What it can do
Review code changes in pull requests
Pull request with code diffs → Code review comments and suggestions
Analyze code diffs for potential issues
Code diff or patch file → List of identified bugs, security vulnerabilities, and code smells
Suggest code improvements and optimizations
Code snippets or files → Specific improvement recommendations with explanations
Check code adherence to best practices
Source code files → Best practice compliance report with violations highlighted
Generate automated code review summaries
Multiple code changes or commits → Comprehensive review summary with key findings
Identify potential security vulnerabilities
Code changes or complete codebase → Security assessment report with vulnerability details
Validate code style and formatting consistency
Source code files → Style guide compliance report with formatting suggestions
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