Python Developer AI Toolkit, Part 1: How I stopped rewriting the same prompts and packaged 272 that actually work

Every time I started a Python code review or backend debugging session, I'd spend 10-15 minutes writing the prompt. Not the review — the prompt . Then I'd get inconsistent output, tweak the prompt, run it again, get something different. Same thing with API architecture. Same thing with test coverage gaps. I was spending more time on prompt engineering than on the actual problem. At some point I started keeping notes. "This phrasing produced good output." "This framing gets consistent code review feedback." Three months in, I had 400+ prompt drafts in a doc. Then I cleaned them up. Tested each one. Cut the ones that didn't produce reliable output. Organized what was left by task type. 272 prompts survived. This is the first in a series on AI-augmented Python development workflows. Part 1 covers the prompt library. Part 2 covers the five CLI scripts that run these prompts as automation tools. What makes a prompt reusable vs. a one-off The prompts that consistently underperformed had two