AI Is Too Eager to Write Code—Ponytail Teaches It to Be Lazy First

AI programming tools are very eager—so eager that they will rewrite an input box that already exists in the project. Ponytail flips this: first, it makes the model pause and ask: Should this feature be built at all? Is there an existing implementation in the project? Can the standard library handle it directly?

It is a prompt and rule pack for AI programming tools. The original post says it already has 180k+ stars on GitHub and supports 20 agents. Claude Code and Codex can be installed as plugins; for tools like Cursor and Gemini, just copy the rule files into the project. Open-source MIT license, free. GitHub link at the end.

The pain point is very concrete. Ask AI to add a date picker, and the model might install a library, or hand-write an entire calendar, ending up with 335 lines. Under Ponytail's constraints, it first discovers that the project already has an input component, and finishes in 10 lines.

What it does includes:

Before writing code, ask first: Is the feature necessary? Does the project already have an implementation? Can the standard library be used directly?
Keep necessary validation, error handling, and security logic. For code involving branches and money amounts, leave a small test along the way.
/ponytail-review reviews changes, including files not changed but affected.
/ponytail-audit scans the entire repository and gives an order for what to fix first.
In Claude Code, you can also see how much money and how many tokens each task costs. A token can be roughly understood as the billing unit for the content the model reads and writes.

The author tested 39 tasks in Claude Code. The results: 53% less code, 41% less time, and 26% lower cost. Saving lines is only superficial; what's really saved is review paths and rework probability. No matter how much AI writes, a human still has to review it in the end.

It suits two kinds of people: independent developers who use AI to write code every day and care about token costs; and people who take over AI-written projects and want to know first where problems are most likely to occur.

There is one detail worth probing. Some commenters said in replies that, rather than saving lines, they care more about the repair order given by /ponytail-audit: is it sorted by impact scope or by change cost? This determines which part to touch first when taking over someone else's project. Tools can move "write less" to before action, but the ordering judgment still requires human attention.

Original post: https://x.com/Sukiea1008/status/2117251379911860597
GitHub: http://github.com/DietrichGebert/ponytail