Automated AI Code Review with GitHub and AACFlow

Running security review as a separate parallel branch is intentional. Security findings need a different system prompt and a lower threshold for flagging — something that looks fine in a general review can be a significant vulnerability. Separating the concerns keeps both prompts focused and the findings clearer.

Setting Up the GitHub Webhook in AACFlow

In AACFlow, navigate to your workflow canvas and add a GitHub Webhook Trigger block. The block generates a unique endpoint URL and secret. Copy both.

In your GitHub repository settings, go to Webhooks → Add webhook. Set:

• Payload URL: the AACFlow endpoint URL
• Content type: application/json
• Secret: the AACFlow-generated secret
• Events: select "Pull requests" only (reduces noise from other event types)

AACFlow automatically validates the X-Hub-Signature-256 header on every incoming webhook event. If the signature does not match, the workflow does not execute — your endpoint is safe without additional authentication.

For the GitHub API calls (fetching diffs, posting comments), add a GitHub credential in AACFlow's credential store with a Personal Access Token or GitHub App installation token scoped to pull_requests: write and contents: read.

Prompt Engineering for Useful Code Review

The most common failure mode in AI code review is over-verbosity — the model flags every minor style inconsistency and produces a wall of comments that reviewers learn to ignore. The system prompt is the primary lever for controlling this.

A system prompt that consistently produces actionable review output:

The explicit "Do NOT comment on" section is essential. Without it, Claude will dutifully report everything it notices, most of which is noise.

Security Review: A Separate Prompt

The security scan runs in a parallel Agent block with a distinct system prompt focused on vulnerability classes:

The "confirmed or high-probability only" instruction reduces false positives. Security noise is particularly expensive — developers who see too many false positives start marking security findings as resolved without reading them.

Adding Language-Specific Conventions

For teams with a consistent codebase, adding language-specific conventions to the general review prompt significantly improves signal quality. For a TypeScript codebase using React and TanStack Query:

Keep the convention list short — five to ten rules maximum. Long convention lists cause the model to focus on conventions rather than logic errors.

Performance Anti-Patterns Block

A third parallel review branch scans specifically for performance anti-patterns — useful for codebases where performance regressions are a recurring problem:

This branch runs only when the diff touches API routes or database query files — a condition filter in AACFlow using a Function block to check the changed file paths before invoking the Agent block.

The GitHub Comment Format

AACFlow formats the merged findings as a single GitHub review comment with collapsible sections, keeping the PR timeline clean:

The comment is posted as a single review (not individual line comments) to avoid cluttering the PR timeline. Individual line comments are more precise but create noise in the GitHub notifications stream for large PRs.

Metrics: What to Expect

Teams that have deployed AI code review with AACFlow report consistent results over a 90-day period:

• 40% reduction in review cycle time: reviewers spend less time on mechanical issues and more time on architecture and design decisions.
• 23% more bugs caught before merge: the AI catches issues in code paths that human reviewers skim or miss because they are familiar with the codebase.
• Reduced review anxiety in junior engineers: knowing the AI has already checked the obvious issues, junior developers submit PRs with more confidence.
• Approximately 0.8–1.2% false positive rate on security findings with a well-tuned prompt — low enough that developers read and act on security comments.

The cost of running Claude Sonnet 4.6 on a typical PR diff (500–2000 lines changed) is $0.003–$0.015 per review. For a team submitting 50 PRs per day, the monthly cost is under $25.

Getting Started

The fastest path to a working AI code review workflow in AACFlow:

1. Create a new workflow and add the GitHub Webhook Trigger block
2. Copy the endpoint URL and secret to your GitHub repository webhook settings
3. Add a GitHub credential (Personal Access Token with pull_requests: write, contents: read)
4. Add the HTTP block to fetch the diff: GET https://api.github.com/repos/{owner}/{repo}/pulls/{pull_number}/files
5. Add the Agent block with Claude Sonnet 4.6 and paste the general review system prompt
6. Add the HTTP block to post the review comment: POST https://api.github.com/repos/{owner}/{repo}/issues/{pull_number}/comments
7. Test by opening a draft PR in your repository

The security scan and performance review branches can be added incrementally. Start with general review working end-to-end before adding the parallel branches.

AI code review is not a replacement for human reviewers. It is a first pass that raises the floor of every pull request before a human ever looks at it.