driftcode-auditor 0.2.0

Local-first CLI for auditing AI-generated code for common mistakes

driftcode-auditor

Local-first CLI for auditing code maintainability, privacy risks, and architecture drift.

License: MIT — Free to use, modify, and distribute.

Features

• Automatically respects .gitignore and common build directories (.next, node_modules, dist, .git, etc.)
• Parallel scanning for good performance on large codebases
• Reports files scanned + time taken
• --stream mode to print issues as they are discovered
• --quiet and --verbose output modes
• Customizable file extensions via --ext or config file
• Configurable PII allowlist to reduce false positives
• Pure local operation — no network calls

Configuration

• Project-level: .driftcode.json in the scanned directory
• User-level: ~/.config/driftcode/config.json

Usage

Basic scan (after installation):

driftcode-auditor --path /path/to/project --format md --privacy

Or run directly from source:

python -m driftcode_auditor --path /path/to/project --format md --privacy

Common options:

driftcode-auditor --path . --format md --privacy --maintainability
driftcode-auditor --path . --format json --privacy --stream
driftcode-auditor --path . --privacy --verbose
driftcode-auditor --path . --privacy --ext .py,.ts

Flags

Flag	Description
--path	Directory to scan (default: current dir)
--format	Output format: md or json
--privacy	Enable privacy risk detection
--maintainability	Enable maintainability checks
--stream	Print issues immediately as found
--output	File for full detailed report (default: driftcode-report.md)
--quiet	Minimal output
--verbose	Show every file being scanned
--ext	Comma-separated list of extensions to scan

Example Output

Scanning /home/user/project ... (skipping common build dirs)

# DriftCode Auditor Report
...

## Privacy
- **pii** in `lib/network.ts:42`: PII in code
- ... and 47 more

## Architecture
- **large_file** in `lib/commands.ts:1`: File >500 lines
- ... and 3 more

Scan complete.
Files scanned: 1247
Time taken: 3.21s
Total issues found: 87

When many issues are found, only the first 15 per category are shown, followed by a summary count.

Running Tests

python -m pytest tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support the Project

DriftCode Auditor is free and open source, and will remain that way.

If you find it valuable — especially when auditing AI-generated code — consider sponsoring its development on GitHub Sponsors.

Your support helps sustain focused work on the tool, funds new rule development, and keeps the project moving forward as AI coding practices evolve.

Every sponsor directly contributes to making reliable AI code review more accessible.

Philosophy

DriftCode Auditor is designed to help developers audit AI-generated code for common and obvious mistakes.

Key goals:

• Catch simple issues that would otherwise go unnoticed
• Be fast and respectful of existing project structure (respects .gitignore)
• Stay simple, local-first, and privacy-conscious
• Provide clear, actionable feedback without false security

All scanning happens on your machine with no data leaving your environment.

Reviewing AI-Generated Code

DriftCode Auditor is designed to help you quickly audit code produced by AI coding assistants (Claude, Cursor, Copilot, etc.).

Common Issues It Catches

• Missing or fake error handling
• Hardcoded secrets and credentials
• Overly generic function names
• Architectural drift and pattern violations
• Missing edge case handling
• Potential PII / secret leaks

Recommended Workflow

1. Generate code with your AI assistant
2. Run driftcode-auditor on the changed files
3. Review flagged issues before committing
4. Use --output report.md for a full detailed report

Example: Catching a Common AI Mistake

AI-generated code:

def get_user(user_id):
    return db.query("SELECT * FROM users WHERE id = " + user_id)

DriftCode Auditor output:

- **pii** in `user_service.py:12`: PII in code → `return db.query("SELECT * FROM users WHERE id = " + user_id)`
- **secret** in `user_service.py:12`: Potential SQL injection risk

This helps catch problems that are easy to miss in normal code review.

This adds a lightweight but effective safety net when working with AI-generated code.