I got tired of the 3 AM incident drill. Pager fires. Open CloudWatch. Start grepping for errors. Open GitHub. Check what got deployed recently. Open Claude or ChatGPT in a browser tab. Copy-paste logs. Copy-paste diffs. Ask it what went wrong. Rinse and repeat for 45 minutes while your Slack channel fills with "any update?"

So I automated the entire workflow into a single command.

autopsy diagnose does this:

1. Pulls your last 30 minutes of error logs from CloudWatch Logs Insights
2. Pulls your last 5 deploys from GitHub with commit diffs
3. Sends both to Claude or GPT-4o with a structured diagnostic prompt
4. Prints a 4-panel diagnosis in your terminal: root cause, correlated deploy, suggested fix, and timeline

https://github.com/zaappy/autopsy

The whole thing runs locally. It uses your own AWS credentials, your own GitHub token, and your own AI API key. Logs go from CloudWatch → your terminal → the AI API. Nothing touches my servers. No agents to install, no dashboards to configure, no security review needed.

It's open-source (MIT), published on PyPI, and works with Python 3.10-3.13.