Build an AI Code Review Agent for Pull Requests
Design an AI agent that automatically reviews GitHub pull requests, checks for bugs, security issues, and style violations, then comments with actionable feedback.
Create an automated code review system that catches common issues before human review, reducing review time and improving code quality.
At a glance
Access
Free prompt
Open to copy without upgrading.
Prompt objective
Create an automated code review system that catches common issues before human review, reducing review time and improving code quality.
Real use case
A development team of 12 engineers reviews 30 PRs per week. Senior engineers spend 40% of review time on style issues and obvious bugs that an AI could catch.
Customize these fields first
Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.
Prompt
Act as a senior software engineer and AI architect. Design an AI code review agent for [COMPANY NAME]'s [LANGUAGE/FRAMEWORK] codebase.\n\n**Context:**\n- Repository: [GITHUB URL]\n- Languages: [LANGUAGE 1], [LANGUAGE 2]\n- PR volume: [NUMBER] per week\n- Team size: [NUMBER] engineers\n- Existing linting: [ESLINT/PYLINT/OTHER]\n\n**Deliverables (numbered):**\n1. Agent architecture: trigger (GitHub webhook on PR open/update), analysis pipeline (parse diff, run static analysis, apply AI review rules), comment generation, and posting\n2. Review categories with severity levels:\n - Critical: security vulnerabilities, potential data loss, race conditions\n - Warning: performance issues, error handling gaps, deprecated APIs\n - Suggestion: naming conventions, code duplication, readability improvements\n3. Context awareness: how the agent understands project conventions (reads CONTRIBUTING.md, existing patterns, architecture docs) to give relevant feedback\n4. Comment format: structured comments with file:line reference, issue description, severity, suggested fix (with code snippet), and 'why this matters' explanation\n5. False positive reduction: rules to skip generated files, test files, migration files, and known patterns; confidence threshold for posting\n6. Learning loop: track which suggestions are accepted/rejected by reviewers, adjust weights over time\n7. Integration details: GitHub App setup, required permissions, rate limit handling, and fallback when AI service is unavailable\n\n**Constraints:**\n- Must never block PRs -- comments only, no status checks\n- Must respect existing linter rules (don't duplicate what ESLint already catches)\n- Must handle large PRs (>500 lines) by prioritizing critical issues first
Open directly in an AI — the text is pre-filled:
How to use this prompt
- 1Replace the key placeholders first: COMPANY NAME, LANGUAGE/FRAMEWORK, GITHUB URL, LANGUAGE 1.
- 2Replace any bracketed placeholders like [this] with your own context.
- 3Add extra background information when you want more tailored results.
- 4Combine multiple prompts in one conversation when you need a richer output.
- 5Save your best-performing prompts so they are easy to reuse later.
Next best step
Open the guide first, then branch only if you still need more.
A fast starting guide for professionals who want to get AI working in the real world in less than a week.
If this prompt is close but not quite right, generate variants next. If the job is recurring, move into the course library after the guide.
Related prompts
View allDesign an AI Research Agent for Market Analysis
Create a specification for an AI agent that autonomously researches market trends, compiles competitor data, and generates weekly market intelligence reports.
Best for
Design an autonomous research agent that replaces 10 hours/week of manual market research with structured, actionable intelligence.
Create an AI Customer Support Triage Agent
Design an AI agent that reads incoming support tickets, classifies them by urgency and category, suggests responses, and routes to the right team.
Best for
Build an intelligent triage layer that reduces first-response time and ensures tickets reach the right team with context.
Design an AI Sales Outreach Agent
Build an AI agent that researches prospects, writes personalized outreach emails, sequences follow-ups, and updates the CRM with engagement data.
Best for
Automate the top-of-funnel outreach process while maintaining personalization quality that matches human-written emails.
Build an AI Data Analysis Agent for Business Metrics
Design an AI agent that connects to a database, answers natural language questions about business data, generates charts, and explains insights.
Best for
Enable non-technical team members to query business data conversationally without writing SQL or using BI tools.
Free browsing stays open. Premium prompts unlock the reusable workflow layer.
Use the guides and role paths to validate the job first. Upgrade when you want the full prompt text, editable premium prompts, and the surrounding course paths in one place.
Free access
- Browse guides, role paths, and category pages.
- Preview prompts before you decide to upgrade.
- Find the right starting point without friction.
Membership access
- Unlock premium prompts and the full copy text.
- See more workflow paths and course connections.
- Keep the reusable templates in one place.
