The gap
Every new Claude user writes weak prompts at first — but the hard part isn't fixing them, it's knowing what's wrong. The cookbook has great evaluation recipes, but none that take a messy real-world prompt, diagnose it, and repair it. I'd love to contribute that recipe.
What it does (before → after, in one notebook)
Input: "fix my resume idk make it good" Output: a scored diagnosis + a rewritten, structured prompt that measurably scores higher.
Concrete outline
- Score — Claude grades a draft against 5 best-practice dimensions (role, context, specificity, format, structure) and returns a structured JSON rubric with a per-dimension reason for any low score.
- Repair — a single "prompt compiler" call rewrites the draft into a well-structured prompt, preserving the user's intent.
- Prove it worked — re-score the rewritten prompt and show the lift; run it over a small batch to report average improvement (a real eval loop, not a vibe check).
- Make it reusable — wrap it in a drop-in
score()/improve()helper readers can paste into their own apps.
Why it's worth merging
- Fills a genuine gap (diagnose-and-repair, not just evaluate).
- Immediately practical — anyone building on Claude wants "make my users' prompts better."
- Self-contained, matches your existing notebook style, easy to review.
Why I can deliver it cleanly
I already built these exact ideas into a working, open-source browser extension — with 100+ passing tests, reproducible benchmarks, and design docs: https://github.com/Amritha902/promptcomplete I'll adapt the concepts into a polished, standalone notebook.
Try it live (no install): https://claude.ai/code/artifact/4d15fab9-3403-4cba-b916-a4442b0768b7
Does this fit, and is anything similar already planned? Happy to tune the scope to whatever you'd find most useful. I can have a draft ready quickly.
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