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Review GitHub pull requests with AI and log results to PostgreSQL and Slack

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Created by: Oneclick AI Squad || oneclick-ai

Oneclick AI Squad

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Last update 2 days ago

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Automatically detects new GitHub Pull Requests, analyzes changed code with AI, generates detailed review comments (quality, security, performance, best practices), posts suggestions back to the PR, stores results in a database, and sends notifications.

Good to Know

  • Triggers automatically on new/updated GitHub Pull Requests via webhook (or manual test)
  • Fetches only changed files/diffs — no need to clone full repo
  • Uses AI (Grok, OpenAI, Claude, Gemini, etc.) to provide intelligent, context-aware feedback
  • Covers multiple dimensions: code quality, bugs, security vulnerabilities, performance issues, maintainability, style/best practices
  • Posts formatted review comments directly on the GitHub PR (with severity levels, suggestions, code snippets)
  • Stores review history & scores in PostgreSQL (or other DB) for auditing, metrics, team dashboards
  • Sends real-time notifications (Slack, Discord, email, etc.) for high-severity findings
  • Saves developers hours on initial reviews and catches issues early

How It Works

1. Trigger PR Detection

  • GitHub Webhook node — listens for pull_request events (opened, synchronize, reopened, ready_for_review)
  • Optional: Filter node to ignore drafts, dependabot PRs, or specific branches
  • Manual trigger available for testing

2. Fetch & Analyze Code

  • GitHub node — retrieves PR details (title, body, number, repo, base/head commits)
  • GitHub or HTTP Request — fetches list of changed files + diffs (using GitHub API /pulls/{number}/files and diff content)
  • Merge PR Details & Extract Diffs — combines metadata + code changes into structured format
  • Prepares payload: file paths, diff hunks, full file content if needed (truncated for large files)

3. AI Review & Score

  • Sends prepared diff data + context (language, repo conventions, custom guidelines) to AI model
  • Prompt engineering focuses on:
    • Code correctness & bugs
    • Security vulnerabilities (OWASP, secrets, injection risks)
    • Performance optimizations
    • Readability, maintainability, SOLID principles
    • Best practices & style (specific to language/framework)
    • Refactoring suggestions with examples
  • AI returns structured output: severity (low/medium/high/critical), category, comment text, suggested fix (with code block)
  • Optional: Score node — assigns overall PR quality score (0–100) based on findings

4. Post Review & Notify

  • Route by severity / issue count (e.g. critical → immediate Slack)
  • GitHub node — posts detailed review comments on the PR (as bot user)
    • Supports threaded replies, line-specific comments (if hunk positions available)
    • Adds label e.g. ai-reviewed, needs-changes
  • Store Results in PostgreSQL — logs full review (PR link, timestamp, AI output JSON, score, issues list)
  • Send Summary to Slack (or Discord/Email/Telegram) — concise message with key findings, link to PR, severity highlights
  • Log Completion — records successful execution for monitoring

Data Sources

  • GitHub — Pull Requests, diffs, comments, labels (via webhook + API)
  • AI Model — Grok (xAI), OpenAI GPT-4o / o1, Anthropic Claude, Google Gemini, or local LLM
  • Storage — PostgreSQL (recommended for structured querying), or Supabase, Airtable, Google Sheets
  • Notifications — Slack, Discord, Microsoft Teams, Email (SMTP), Telegram

How to Use

  1. Import the workflow JSON into your n8n instance
  2. Configure credentials:
    • GitHub OAuth / Personal Access Token (with repo scope)
    • AI provider API key (Grok/OpenAI/etc.)
    • PostgreSQL database connection
    • Slack/Discord/Email credentials
  3. Set up GitHub Webhook:
    • In repo Settings → Webhooks → Add webhook
    • Payload URL = your n8n webhook URL
    • Content type: application/json
    • Events: Pull requests
  4. Customize AI prompt — add repo-specific rules, coding standards, ignored patterns
  5. Tune filters — minimum severity to post, files to skip (e.g. lock files, generated code)
  6. Test — create/open a small PR or use Execute Workflow with sample payload
  7. Activate — turn on the workflow and monitor Executions + Logs

Requirements

  • n8n (self-hosted preferred for webhooks)
  • GitHub repo with admin access to add webhook & bot token
  • AI API access with sufficient token limit (large PRs = large prompts)
  • PostgreSQL database (or alternative) for persistent storage
  • Notification service account (Slack app, Discord bot, etc.)

Customizing This Workflow

  • Add custom best practices — load from Google Sheets/Notion/Airtable and inject into prompt
  • Support multi-file analysis — chunk very large PRs or summarize per-file first
  • Auto-approve low-risk PRs — add approval action if score > 90 and no critical issues
  • Security focus — integrate with tools like Semgrep/Trivy results
  • Comment on specific lines — use GitHub API position/hunk data for inline comments
  • Team routing — notify language-specific experts via Slack channels
  • Metrics dashboard — connect DB to Grafana/Metabase for review trends
  • Ignore patterns — skip vendor/, node_modules/, tests/, etc.
  • Multiple AI models — fallback or ensemble (e.g. Claude for reasoning + Grok for speed)