Git hooks are one of the most powerful — and most underutilized — features in the Git ecosystem. They allow you to automate actions at key points in your Git workflow: before committing, before pushing, after merging, and more. Traditionally, these hooks are implemented using shell scripts, but that’s limiting in today’s landscape of rich APIs and AI-assisted development.

In this article, we’ll explore how to supercharge Git hooks using Python and OpenAI’s GPT models. You'll learn how to build intelligent pre-commit and pre-push hooks that lint your code, enforce commit message standards, and even review your changes for risky patterns — all before the code leaves your machine.

Why Move Beyond Bash?

Bash is great for simple checks like formatting or linting. But what if your hook needs to:

• Analyze the meaning of a code change?
• Flag insecure configurations based on context?
• Suggest improvements or explain logic?
• Enforce commit message standards dynamically?

This is where Python shines, especially when combined with tools like OpenAI’s API and GitPython.

Git Hook Basics (A Quick Refresher)

Git hooks live in the .git/hooks/ directory of every Git repo. You can create scripts there with specific names like:

• pre-commit — runs before git commit
• prepare-commit-msg — edits the message before the editor opens
• pre-push — runs before pushing to a remote

These scripts must be executable:

chmod +x .git/hooks/pre-commit

And they must exit with code 0 to succeed or 1 to abort the action.

Now let’s build something more powerful than a shell script.

Project: AI-Powered Pre-Commit Hook in Python

Our goal is to build a Python-based pre-commit hook that:

1. Analyzes the staged diff
2. Uses OpenAI to review the code for potential issues
3. Blocks the commit if high-risk patterns are found

Step 1: Install Required Tools

Install Python packages:

pip install openai gitpython

Set your OpenAI API key:

export OPENAI_API_KEY=sk-...

Step 2: Create the Pre-Commit Hook

Save the following script as .git/hooks/pre-commit and make it executable:

#!/usr/bin/env python3

import os
import openai
from git import Repo

openai.api_key = os.getenv("OPENAI_API_KEY")

repo = Repo(".")
diff = repo.git.diff('--cached')

if not diff.strip():
    print("Nothing staged. Skipping AI analysis.")
    exit(0)

prompt = f"""
You are a senior software engineer. Review the following Git diff for potential 
security or logic issues. Be concise. Only report if there's a clear problem.

{diff}
"""

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}]
)

feedback = response['choices'][0]['message']['content']
if "no issues found" in feedback.lower() or "looks good" in feedback.lower():
    print("AI Review Passed ✅")
    exit(0)

print("⚠️ AI Feedback:")
print(feedback)
print("❌ Commit blocked based on AI analysis. Resolve or bypass manually.")
exit(1)

What This Does

• Captures staged changes only (git diff --cached)
• Sends the diff to GPT-4 with a code review-style prompt
• Blocks the commit if GPT returns a warning or concern

You can easily modify this to only warn and not block — just change exit(1) to exit(0).

Bonus: Auto-Generate Commit Messages with AI

You can also use the prepare-commit-msg hook to generate smart commit messages:

#!/usr/bin/env python3

import os
import openai
from git import Repo

openai.api_key = os.getenv("OPENAI_API_KEY")

repo = Repo(".")
diff = repo.git.diff('--cached')

prompt = f"Write a concise Git commit message for this diff:\n\n{diff}"

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}]
)

msg_path = os.sys.argv[1]
with open(msg_path, "w") as f:
    f.write(response['choices'][0]['message']['content'].strip() + "\n")

Make this your .git/hooks/prepare-commit-msg, and every commit will be auto-documented by your friendly AI pair programmer.

Advanced Ideas

• Team Policy Enforcement: Block commits that include console.log() or debug=true
• Context-Aware Suggestions: Use LLMs to suggest test cases for new logic
• Custom Rules: Create a local .ai-lint.json that defines project-specific red flags
• Language-Specific Reviews: Tailor prompts for Python, TypeScript, Go, etc.

Git Hook Best Practices

• Hooks are local-only — they don’t run on teammates’ machines unless shared via tools like Husky or in monorepos via CI.
• Always log your hooks clearly — unclear errors can frustrate devs.
• Provide bypass options (e.g., --no-verify) for emergencies or debugging.

Conclusion

By extending Git hooks with Python and OpenAI, you’re unlocking the next evolution of developer tooling — automation with context, intelligence, and adaptability. Whether you want to build safer workflows, save time, or just impress your team, this is a powerful technique to keep in your engineering toolkit.

Want more LLM-powered workflows and Git tooling hacks? Explore more at Slaptijack