ponytail

Ponytail

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

80-94% less code · 3-6× faster · 42-75% cheaper
_{Per-task code, latency, and cost on the Claude API, not your plan's quota. Median across Haiku, Sonnet, and Opus (10 runs for code and latency, 30 for the re-verified cost). Results vary by model and prompt: the ruleset re-injects each turn, so on a short prompt or a terse reasoning model that overhead can outweigh the savings. Reproduce it yourself.}

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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

Five everyday tasks (email validator, debounce, CSV sum, countdown timer, rate limiter), three models, three arms: no skill, the caveman skill, and ponytail. Ten runs per cell, median reported.

80-94% less code, 42-75% less cost, and 3-6× faster than a no-skill agent, on every Claude model. Every shortcut ponytail takes is marked in the code with a ponytail: comment naming its upgrade path. Reproduce it yourself: npx promptfoo eval -c benchmarks/promptfooconfig.yaml. Method and raw numbers: benchmarks/. Production-grade tasks, where an unconstrained agent bloats far more, are written up in benchmarks/results/.

That is the byproduct, not the pitch. These are Claude numbers, and they vary by model. Capable instruction-following models follow the ladder and write far less, cheaper and faster. Terse reasoning models can go the other way: the ladder is a deliberation step, so the model spends thinking tokens working through the rungs before it saves any output, and together with the always-on ruleset that can cost more than the shorter code saves. On GPT-5.5 it does. And all of this is single-shot, one prompt in and one answer out: a real agent session re-injects the ruleset and runs the ladder every turn, which this benchmark does not measure, so per-session cost can land either way. 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, and that is the part that stays maintainable. Lower cost and latency are a side effect on the models that follow it.

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. Stdlib does it?            → use it
3. Native platform feature?   → use it
4. Installed dependency?      → use it
5. One line?                  → one line
6. Only then: the minimum that works

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

Codex

codex plugin marketplace add DietrichGebert/ponytail
codex

Open /plugins, select the Ponytail marketplace, and install Ponytail. Then open /hooks, review and trust its two lifecycle hooks, and start a new thread.