●MAX — Rork Max builds native Swift apps instead of React Native, supporting iPhone, iPad, Watch, TV, Vision Pro, and iMessage●NATIVE — It unlocks native capabilities: AR/LiDAR, Metal 3D games, Dynamic Island, Live Activities, HealthKit, and Core ML●CORE — Standard Rork generates iOS/Android apps with React Native (Expo), taking you from plain English to the app stores●FUNDING — Rork raised $2.8M from a16z (Andreessen Horowitz)●GROWTH — The platform now sees 743,000 monthly visits with 85% growth●PRICING — Free to start, with paid plans from $25/month●MAX — Rork Max builds native Swift apps instead of React Native, supporting iPhone, iPad, Watch, TV, Vision Pro, and iMessage●NATIVE — It unlocks native capabilities: AR/LiDAR, Metal 3D games, Dynamic Island, Live Activities, HealthKit, and Core ML●CORE — Standard Rork generates iOS/Android apps with React Native (Expo), taking you from plain English to the app stores●FUNDING — Rork raised $2.8M from a16z (Andreessen Horowitz)●GROWTH — The platform now sees 743,000 monthly visits with 85% growth●PRICING — Free to start, with paid plans from $25/month
Rork Max × Automated AI Code Review: Continuous Quality Assurance with GitHub Actions + Claude API
Automate AI code review for Rork Max with GitHub Actions and Claude API. Detect bugs, security risks, and performance issues on every PR automatically.
Setup and context: Why AI Code Review Matters for Solo Developers
When developing apps with Rork Max, code quality management can be one of the trickiest challenges to tackle. Working alone means fewer eyes on your code, which makes it easy to miss bugs or security vulnerabilities before they reach production.
Not long ago, code review was considered a team development practice. But AI has changed that entirely. By combining GitHub Actions with the Claude API, you can now set up a system where every Pull Request automatically triggers an AI review — flagging problems before you merge, all within a few hours of setup.
What You'll Learn
How GitHub Actions workflows integrate with the Claude API
Configuring automated AI code reviews for every Pull Request
Automating bug detection, security scanning, and performance analysis
Formatting and posting review results as clear GitHub PR comments
Customization strategies tailored for solo and team development contexts
Who This Is For
Developers building React Native / Expo apps with Rork Max
Solo developers shipping apps who want better confidence in code quality
Developers who have CI/CD in place but haven't automated code review yet
Anyone interested in practical development automation with the Claude API
System Architecture Overview
Before diving in, let's map out the full flow of what we're building.
The pipeline works as follows: a developer opens a Pull Request, which triggers GitHub Actions. The workflow fetches the diff of changed files and sends it to the Claude API. The AI analyzes the code for bugs and logic errors, security risks such as exposed API keys and XSS vulnerabilities, performance issues like unnecessary re-renders and incorrect useEffect dependency arrays, React Native best practice violations, and TypeScript type safety problems. The review results are then automatically posted as a comment on the PR, where the developer can review them and make fixes.
This means every single PR gets an AI-powered review before it's merged — catching issues that human eyes often miss.
Key Benefits
Always-on coverage: Even code written at midnight gets reviewed immediately
Consistent quality standards: Reviews are never affected by tiredness or distraction
Built-in learning: Developers naturally improve by reading AI feedback over time
Cost-efficient: Comparable quality to human review at a fraction of the time cost
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WHAT YOU'LL LEARN
✦Learn the complete implementation of PR-level automated review using GitHub Actions and Claude API
✦Understand how to design workflows that automate bug detection, security scanning, and performance analysis
✦Discover practical customization strategies for applying AI code review to both solo and team projects
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A Claude API key (from the Anthropic developer dashboard)
Admin access to the GitHub repository (required for Secrets configuration)
Configuring GitHub Secrets
The first step is to securely store your Claude API key in GitHub Secrets so it never appears in your codebase.
Navigate to your repository's Settings, then Secrets and variables → Actions, and click New repository secret. Enter ANTHROPIC_API_KEY as the name and paste your API key as the value.
# Secret configuration (set via GitHub UI)Name: ANTHROPIC_API_KEYValue: YOUR_ANTHROPIC_API_KEY
Step 2: Creating the Workflow File
Create a .github/workflows/ directory in your project root and add the following workflow file.
# .github/workflows/ai-code-review.ymlname: AI Code Reviewon: pull_request: types: [opened, synchronize, reopened] paths: - 'app/**' - 'components/**' - 'hooks/**' - 'lib/**' - 'utils/**' - 'api/**' - '*.ts' - '*.tsx'jobs: ai-code-review: name: AI Code Review with Claude runs-on: ubuntu-latest permissions: contents: read pull-requests: write # Required for posting PR comments steps: - name: Checkout repository uses: actions/checkout@v4 with: fetch-depth: 0 # Full history required to compute diffs - name: Set up Node.js uses: actions/setup-node@v4 with: node-version: '20' - name: Install dependencies for review script run: npm install @anthropic-ai/sdk @octokit/rest - name: Run AI Code Review env: ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} PR_NUMBER: ${{ github.event.pull_request.number }} REPO_OWNER: ${{ github.repository_owner }} REPO_NAME: ${{ github.event.repository.name }} BASE_SHA: ${{ github.event.pull_request.base.sha }} HEAD_SHA: ${{ github.event.pull_request.head.sha }} run: node .github/scripts/ai-review.js
Step 3: Implementing the AI Review Script
Now let's create the script that calls the Claude API and handles the review.
// .github/scripts/ai-review.jsconst Anthropic = require('@anthropic-ai/sdk');const { Octokit } = require('@octokit/rest');const { execSync } = require('child_process');const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN });// Retrieve the code diff for changed filesfunction getDiff() { try { const baseSha = process.env.BASE_SHA; const headSha = process.env.HEAD_SHA; const diff = execSync( `git diff ${baseSha}...${headSha} -- '*.ts' '*.tsx' '*.js' '*.jsx'`, { maxBuffer: 1024 * 1024 * 10 } ).toString(); // Truncate very large diffs to save tokens const maxLength = 15000; if (diff.length > maxLength) { return diff.substring(0, maxLength) + '\n\n... (diff truncated due to length)'; } return diff; } catch (error) { console.error('Failed to retrieve diff:', error.message); return null; }}// Call Claude API to perform the code reviewasync function reviewWithClaude(diff) { const systemPrompt = `You are an expert in React Native, Expo, and Rork Max app development.Please review the following code diff with attention to:1. Bugs and logic errors: null access, type mismatches, async handling issues, state update problems2. Security risks: hardcoded API keys, improper permissions, XSS vulnerabilities3. Performance issues: unnecessary re-renders, incorrect useEffect dependency arrays4. React Native best practices: Platform.OS branching, StyleSheet usage5. TypeScript: overuse of any, missing type definitionsReturn the review in the following format:## AI Code Review### Critical Issues (must fix)(Description and location of each issue)### Recommended Improvements(Areas to improve)### Suggestions and Best Practices(Better implementation approaches)### What's Working Well(Positive aspects of the code)If no issues are found, state: "No significant issues found."Do not use Markdown tables.`; const response = await anthropic.messages.create({ model: 'claude-opus-4-6', max_tokens: 2048, messages: [ { role: 'user', content: `Please review the following code diff:\n\n\`\`\`diff\n${diff}\n\`\`\``, }, ], system: systemPrompt, }); return response.content[0].text;}// Post the review as a GitHub PR commentasync function postReviewComment(reviewText) { const owner = process.env.REPO_OWNER; const repo = process.env.REPO_NAME; const prNumber = parseInt(process.env.PR_NUMBER, 10); // Check for existing AI review comments to avoid duplicates const { data: comments } = await octokit.issues.listComments({ owner, repo, issue_number: prNumber, }); const existingComment = comments.find( (comment) => comment.user.login === 'github-actions[bot]' && comment.body.includes('AI Code Review') ); const commentBody = `${reviewText}\n\n---\n*This review was automatically generated by Claude AI.*`; if (existingComment) { // Update existing comment instead of creating a duplicate await octokit.issues.updateComment({ owner, repo, comment_id: existingComment.id, body: commentBody, }); console.log('Updated existing AI review comment'); } else { await octokit.issues.createComment({ owner, repo, issue_number: prNumber, body: commentBody, }); console.log('Posted new AI review comment'); }}// Main executionasync function main() { console.log('Fetching code diff...'); const diff = getDiff(); if (!diff || diff.trim() === '') { console.log('No diff found. Target files may not have changed.'); return; } console.log(`Diff size: ${diff.length} characters`); console.log('Calling Claude API for code review...'); const reviewText = await reviewWithClaude(diff); console.log('Posting review comment to GitHub...'); await postReviewComment(reviewText); console.log('AI code review completed successfully!');}main().catch((error) => { console.error('An error occurred:', error); process.exit(1);});
Step 4: Adding a Dedicated Security Scan Workflow
In addition to general code review, a security-focused scan adds an extra layer of protection.
Here's a rough estimate of monthly costs when using Claude Opus 4.6. An average PR with around 200 changed lines uses approximately 2,000 input tokens and 500 output tokens, costing around $0.025. At 30 PRs per month, that's roughly $0.75. At 100 PRs per month, you're looking at around $2.50. For solo developers, this is an extremely cost-effective way to maintain code quality.
If you want to reduce costs further, consider using Claude Haiku for the initial review pass and only escalating to Opus for complex issues.
Step 7: Project-Specific Customization
After running the system for a while, you'll want to tune the prompts to better match your project's conventions.
// Add project-specific rules to the system prompt in ai-review.jsconst projectRules = `Additionally, verify the following project-specific conventions:- Supabase client must always be imported from lib/supabase.ts- Environment variables must be defined in .env.local only — never hardcoded- All API response types must be explicitly defined — avoid using 'any'- Components must be placed in the components/ directory`;// Log review results for later analysisconst fs = require('fs');const logEntry = { date: new Date().toISOString(), prNumber: process.env.PR_NUMBER, diffSize: diff.length, hasIssues: reviewText.toLowerCase().includes('critical'), model: 'claude-opus-4-6',};fs.appendFileSync('.github/review-logs.jsonl', JSON.stringify(logEntry) + '\n');
Step 8: Extending for Team Development
When working with a team, a few additional configurations make the system even more powerful.
To automatically block PR merges when critical issues are found, use GitHub's Branch Protection Rules. In your repository's Settings → Branches, add a branch protection rule for main and enable "Require status checks to pass before merging". Select your AI Code Review with Claude workflow as a required check.
For Slack notifications, the approach is similar to the notification setup described in CI/CD Pipeline Setup with GitHub Actions and EAS Build. Adding a step that calls a Slack webhook with the review summary is straightforward.
To prevent parallel reviews from hitting API rate limits, add a concurrency group to your workflow:
This guide covered how to build a complete AI code review pipeline for Rork Max projects using GitHub Actions and the Claude API.
The three pillars of the implementation are securely managing API keys in GitHub Secrets, building a workflow that extracts PR diffs and sends them to Claude for analysis, and automatically posting formatted review results as PR comments. Layering in security scanning and performance analysis creates a comprehensive quality assurance system that runs on autopilot.
For solo developers shipping apps independently, this kind of automation is a genuine force multiplier. Having AI check every code change before it merges dramatically reduces the risk of bugs, security vulnerabilities, and App Store rejections — all without adding review overhead to your workflow.
Advanced: Generating Tests Automatically
One of the most powerful extensions to this pipeline is automatic test generation for changed code. Rather than writing tests after the fact, this workflow generates initial test scaffolding for any new function or component as part of the PR process.
// .github/scripts/test-generator.jsconst Anthropic = require('@anthropic-ai/sdk');const fs = require('fs');const path = require('path');const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });async function generateTests(filePath) { if (!fs.existsSync(filePath)) return null; // Skip if test file already exists const testFilePath = filePath.replace(/\.(ts|tsx)$/, '.test.$1'); if (fs.existsSync(testFilePath)) { console.log(`Skipping — test file already exists: ${testFilePath}`); return null; } const content = fs.readFileSync(filePath, 'utf8'); const response = await anthropic.messages.create({ model: 'claude-haiku-4-5-20251001', // Haiku is sufficient for test generation max_tokens: 1024, messages: [ { role: 'user', content: `Generate Jest tests for the following React Native / TypeScript code.Use @testing-library/react-native and include test cases for the main functions and components.Return only the test code — no explanation needed.\`\`\`typescript${content.substring(0, 3000)}\`\`\``, }, ], }); return { filePath: testFilePath, content: response.content[0].text, };}async function main() { // Read target files from environment variable (comma-separated) const targetFiles = (process.env.TARGET_FILES || '').split(',').filter(Boolean); // Limit to 3 files per run to manage costs for (const file of targetFiles.slice(0, 3)) { console.log(`Generating tests for: ${file}`); const result = await generateTests(file); if (result) { fs.writeFileSync(result.filePath, result.content); console.log(`Tests written to: ${result.filePath}`); } }}main().catch(console.error);
To wire this into your workflow, add a step after the AI review that identifies changed component files and passes them to the test generator:
This doesn't replace writing real tests — but it creates a useful starting point and signals to the team which files need test coverage.
Advanced: Automated PR Description Generation
Another time-saving application of the same infrastructure is auto-generating PR descriptions. Developers often skip writing thorough descriptions under deadline pressure, which makes code history harder to navigate later.
// .github/scripts/pr-description-generator.jsconst Anthropic = require('@anthropic-ai/sdk');const { Octokit } = require('@octokit/rest');const { execSync } = require('child_process');const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN });async function generateDescription(diff) { const response = await anthropic.messages.create({ model: 'claude-haiku-4-5-20251001', max_tokens: 512, messages: [ { role: 'user', content: `Based on the following code diff, write a clear, concise PR description.Include: what changed, why it matters, and any important implementation notes.Use plain paragraphs — no bullet lists or tables.\`\`\`diff\n${diff.substring(0, 8000)}\n\`\`\``, }, ], }); return response.content[0].text;}async function main() { const baseSha = process.env.BASE_SHA; const headSha = process.env.HEAD_SHA; let diff; try { diff = execSync(`git diff ${baseSha}...${headSha}`).toString(); } catch (e) { console.error('Failed to get diff:', e.message); return; } const prNumber = parseInt(process.env.PR_NUMBER, 10); const owner = process.env.REPO_OWNER; const repo = process.env.REPO_NAME; // Only auto-fill if the PR body is empty or minimal const { data: pr } = await octokit.pulls.get({ owner, repo, pull_number: prNumber }); if (pr.body && pr.body.trim().length > 100) { console.log('PR already has a description — skipping auto-generation'); return; } const description = await generateDescription(diff); await octokit.pulls.update({ owner, repo, pull_number: prNumber, body: description }); console.log('PR description updated');}main().catch(console.error);
Advanced: Tracking Code Quality Trends Over Time
Running individual reviews is valuable, but tracking trends across PRs reveals patterns that point to deeper issues in the codebase — areas where the team consistently struggles, or where code quality is improving after focused effort.
// .github/scripts/quality-reporter.js// Generates a weekly code quality report from review logsconst fs = require('fs');const path = require('path');function loadReviewLogs(logFile) { if (!fs.existsSync(logFile)) return []; return fs.readFileSync(logFile, 'utf8') .split('\n') .filter(Boolean) .map((line) => { try { return JSON.parse(line); } catch { return null; } }) .filter(Boolean);}function generateWeeklyReport(logs) { const oneWeekAgo = new Date(Date.now() - 7 * 24 * 60 * 60 * 1000); const weekLogs = logs.filter((log) => new Date(log.date) > oneWeekAgo); if (weekLogs.length === 0) { return 'No reviews in the past week.'; } const totalReviews = weekLogs.length; const reviewsWithIssues = weekLogs.filter((log) => log.hasIssues).length; const issueRate = ((reviewsWithIssues / totalReviews) * 100).toFixed(1); const avgDiffSize = Math.round( weekLogs.reduce((sum, log) => sum + (log.diffSize || 0), 0) / totalReviews ); return [ '## Weekly Code Quality Report', '', `Total reviews this week: ${totalReviews}`, `Reviews with issues flagged: ${reviewsWithIssues} (${issueRate}%)`, `Average diff size: ${avgDiffSize} characters`, '', issueRate > 50 ? 'Note: More than half of PRs had issues flagged. Consider a focused code quality sprint.' : 'Code quality looks healthy this week.', ].join('\n');}const logFile = path.join('.github', 'review-logs.jsonl');const logs = loadReviewLogs(logFile);const report = generateWeeklyReport(logs);console.log(report);// Save report to file for CI artifact uploadfs.writeFileSync('quality-report.md', report);
Add a scheduled workflow to run this reporter and post the summary to a Slack channel or as a GitHub issue comment every Monday morning:
Here's how the full system looks when all the pieces are assembled. At the PR level, code review and security scanning run in parallel. Test generation is optional and triggers only for new component files. PR description generation fills in empty PR bodies automatically. At the repository level, weekly quality reports aggregate the data to surface long-term trends.
Each workflow is independently useful, but together they create a development environment that actively supports code quality rather than just reacting to problems after they're discovered.
The key insight is that none of these automations replace developer judgment — they amplify it. The AI surfaces issues faster than manual review, but you still decide what to fix, what to accept, and what to push back on. Think of it as having a knowledgeable colleague who reviews every PR instantly, without ever getting tired or distracted.
Real-World Tips From Production Use
After running this kind of pipeline across multiple Rork Max projects, a few patterns have emerged that are worth sharing.
First, start with the general code review workflow only and add security and performance scanning once you've validated that the basic setup works for your PR cadence. Adding too many checks at once makes it harder to debug early configuration issues.
Second, the quality of your system prompt matters enormously. Spend time refining it based on the first few dozen reviews. The default prompt in this article is a good starting point, but your specific stack, coding conventions, and team preferences will push you toward a much more targeted version over time.
Third, be intentional about which files trigger reviews. Excluding auto-generated files, build artifacts, and vendored code from the paths configuration in your workflow keeps reviews focused on the code your team actually writes.
Fourth, treat the review logs as a valuable data source. After a month of use, analyzing the logs often reveals consistent problem areas — certain modules or patterns that repeatedly trigger AI warnings. Those are the highest-leverage targets for refactoring or additional documentation.
Finally, communicate clearly with your team about what the AI reviewer is and isn't. It catches a class of mechanical errors very reliably, but it can miss subtle business logic issues, accessibility problems, or UX concerns that require human judgment. Setting these expectations up front prevents over-reliance on the automation.
What's Next
Once this pipeline is running smoothly, several natural extensions are worth exploring.
Integrating with code coverage tools like Istanbul or V8 lets you combine AI review with objective coverage metrics, flagging PRs that drop coverage below a defined threshold alongside any code quality issues.
Adding dependency vulnerability scanning using tools like npm audit or Snyk as an additional workflow step catches security issues introduced by package updates, which the diff-based AI review won't catch since package.json changes don't show vulnerable code directly.
For teams using Expo EAS Build, combining this pipeline with the EAS build workflow described in CI/CD Pipeline Setup with GitHub Actions and EAS Build creates end-to-end automation from code commit to TestFlight distribution — with quality gates at every stage.
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