Ponytail

He says nothing. He writes one line. It works.

~54% less code (up to 94%) · ~20% cheaper · ~27% faster · 100% safe
_{Measured on real Claude Code sessions editing a real open-source repo (FastAPI + React), against the same agent with no skill. ~54% is the mean across 12 feature tasks (Haiku 4.5, n=4); it reaches 94% where an agent over-builds (a date picker) and is near zero where the code is already minimal. ponytail keeps every safety guard while a bare "write one-liners" prompt drops one. (The earlier single-shot benchmark reported 80-94% as a flat figure; against a fair agentic baseline that is the per-task ceiling, not the average.) Full writeup · reproduce it.}

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You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control. You show him fifty lines; he looks at them, says nothing, and replaces them with one.

Ponytail puts him inside your AI agent.

Before / after

You ask for a date picker. Your agent installs flatpickr, writes a wrapper component, adds a stylesheet, and starts a discussion about timezones.

With ponytail:

<!-- ponytail: browser has one -->
<input type="date">

More survivors in examples/.

Numbers

The honest measurement is a real agent doing real work: a headless Claude Code session editing tiangolo's full-stack-fastapi-template (a real FastAPI + React repo), scored on the git diff it leaves behind. Twelve feature tickets, the same agent with and without the skill, n=4, Haiku 4.5.

vs no-skill baseline	LOC	tokens	cost	time	safe
ponytail	-54%	-22%	-20%	-27%	100%
caveman (terse-prose control)	-20%	+7%	+3%	+2%	100%
"YAGNI + one-liners" prompt	-33%	-14%	-21%	-30%	95%

ponytail is the only arm that cuts every metric, and the only one that stays fully safe while doing it. The cut is biggest where there is a real over-build trap (date picker 404 to 23 lines, color picker 287 to 23, because it reaches for a native <input> instead of a component) and near zero on code that is already minimal. Full method, per-task tables, and limitations: benchmarks/results/2026-06-18-agentic.md.

Older single-shot numbers (isolated generation)

Five everyday tasks, three models, three arms (no skill, caveman, ponytail), ten runs, median reported. One prompt, one completion, counting lines of the answer:

This showed 80-94% less code. #126 fairly pointed out that the bare-model baseline pads its answer with prose and options, so that gap is partly a conversational-baseline artifact. The agentic numbers above are the corrected, defensible version. Reproduce the single-shot run with npx promptfoo eval -c benchmarks/promptfooconfig.yaml.

The rule was never "fewest tokens." It is: write only what the task needs, and never cut validation, error handling, security, or accessibility. The code ends up small because it is necessary, not golfed. Lower cost and latency are a side effect on the models that follow the ladder; a terse reasoning model that spends thinking tokens deliberating the rungs can go the other way (on GPT-5.5 it does).

How it works

Before writing code, the agent stops at the first rung that holds:

1. Does this need to exist?   → no: skip it (YAGNI)
2. Already in this codebase?  → reuse it, don't rewrite
3. Stdlib does it?            → use it
4. Native platform feature?   → use it
5. Installed dependency?      → use it
6. One line?                  → one line
7. Only then: the minimum that works

The ladder runs after it understands the problem, not instead of it: it reads the code the change touches and traces the real flow before picking a rung. Lazy about the solution, never about reading.

Lazy, not negligent: trust-boundary validation, data-loss handling, security, and accessibility are never on the chopping block.

Install

The most effort ponytail will ever ask of you:

The Claude Code and Codex plugins run two tiny Node.js lifecycle hooks, so node needs to be on your PATH (note for Nix/nvm users: it must be on the non-interactive shell's PATH). If it isn't, the skills still work, the always-on activation just stays quiet instead of erroring on every prompt.

Claude Code

/plugin marketplace add DietrichGebert/ponytail

/plugin install ponytail@ponytail

(You have to send two separate prompts for the install to work)

Same steps in the Claude Code Desktop app's Code tab: type the two /plugin commands above into the prompt box, or click the + button next to it, choose Plugins → Add plugin to browse your configured marketplaces, and manage marketplaces from Customize in the sidebar.

Codex

codex plugin marketplace add DietrichGebert/ponytail
codex plugin add ponytail@ponytail

Run codex and open /hooks, review and trust its two lifecycle hooks, and start a new thread.

This same install also covers the Codex desktop app: restart the app after installing and it picks up the plugin.

GitHub Copilot CLI

copilot plugin marketplace add DietrichGebert/ponytail
copilot plugin install ponytail@ponytail

In an interactive Copilot CLI session, use the slash equivalents:

/plugin marketplace add DietrichGebert