ponytail

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/logo-dark.png"> <img src="assets/logo.png" width="220" alt="Ponytail, the lazy senior dev"> </picture> </p> <h1 align="center">Ponytail</h1> <p align="center"> <em>He says nothing. He writes one line. It works.</em> </p> <p align="center"> <img src="https://img.shields.io/github/stars/DietrichGebert/ponytail?style=flat-square&color=111111&label=stars" alt="Stars"> <img src="https://img.shields.io/github/v/release/DietrichGebert/ponytail?style=flat-square&color=111111&label=release" alt="Release"> <img src="https://img.shields.io/npm/v/@dietrichgebert/ponytail?style=flat-square&color=111111&label=npm" alt="npm"> <img src="https://img.shields.io/badge/works%20with-20%20agents-111111?style=flat-square" alt="Works with 20 agents"> <img src="https://img.shields.io/badge/license-MIT-111111?style=flat-square" alt="MIT license"> </p> <p align="center"> <a href="https://trendshift.io/repositories/50668" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/50668/daily" alt="DietrichGebert/ponytail | Trendshift" width="250" height="55"/></a> <a href="https://trendshift.io/repositories/50668" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/50668/weekly" alt="DietrichGebert/ponytail | Trendshift" width="250" height="55"/></a> </p> <p align="center"> <strong>~54% less code (up to 94%) &middot; ~20% cheaper &middot; ~27% faster &middot; 100% safe</strong><br> _{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.) <a href="benchmarks/results/2026-06-18-agentic.md">Full writeup</a> &middot; <a href="benchmarks/">reproduce it</a>.} </p> <p align="center"> _{<a href="README.es.md">Español</a> &middot; <a href="README.ko.md">한국어</a>} </p>

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<p align="center"> <a href="https://ponytail.dev/soon"><img src="assets/waitlist-banner.png" alt="Something's coming, join the waitlist" width="760"></a> </p>

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.

<p align="center"> <img src="assets/benchmark-agentic.svg" width="860" alt="Each arm as a percent of the no-skill baseline across LOC, tokens, cost and time (Haiku 4.5). ponytail is lowest on every metric (LOC 46%, tokens 78%, cost 80%, time 73%); caveman rises above 100% on tokens, cost and time; yagni-oneliner LOC 67%. Safety, separate adversarial tier: baseline, caveman and ponytail 100%, yagni-oneliner 95%."> </p>

| 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.

<details> <summary><strong>Older single-shot numbers (isolated generation)</strong></summary>

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

<p align="center"> <img src="assets/benchmark-3model.svg" width="860" alt="Median lines of code per arm across Haiku, Sonnet and Opus"> </p>

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.

</details>

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/ponytail
/plugin install ponytail@ponytail

Copilot CLI namespaces plugin commands by plugin name. For example:

/ponytail:ponytail ultra
/ponytail:ponytail-review

Pi agent harness

pi install git:github.com/DietrichGebert/ponytail

OpenCode

Add to opencode.json:

{ "plugin": ["@dietrichgebert/ponytail"] }

Run from a checkout instead (the plugin reuses hooks/ and skills/):

{ "plugin": ["./.opencode/plugins/ponytail.mjs"] }

Injects the ruleset every turn at the active level; adds the /ponytail commands (see Commands). OpenCode also auto-loads this repo's AGENTS.md, so the rules hold even without the plugin. The plugin adds the lite/full/ultra/off levels.

The ./ path resolves against your project's opencode.json; to share one checkout across projects, point it at the absolute path of the .mjs instead (it finds its hooks/ and skills/ relative to its own file).

Gemini CLI

gemini extensions install https://github.com/DietrichGebert/ponytail

Loads the ruleset as always-on context every session and registers the /ponytail commands; the skills/ ship too, activated when a task needs them. The Gemini adapter intentionally does not ship a root hooks/hooks.json: Gemini auto-loads that path, while Ponytail's lifecycle hooks use Claude/Codex event names.

Qoder

Qoder auto-loads AGENTS.md from the repo root as always-on context, so running ponytail from a checkout works with zero setup. For per-project rules, copy .qoder/rules/ponytail.md into your project's .qoder/rules/. The six ponytail skills (/ponytail, /ponytail-review, /ponytail-audit, /ponytail-debt, /ponytail-gain, /ponytail-help) are available via Qoder's Skill system; the plugin manifest at .qoder-plugin/plugin.json points at the skills/ directory.

For full plugin-tier support (automatic mode activation + ruleset injection on every prompt), add the hooks from hooks/qoder-hooks.json to your .qoder/settings.json. Replace PONYTAIL_DIR with the path to your ponytail checkout. Qoder's UserPromptSubmit hook activates the default mode on first prompt and injects the ruleset every turn; PreToolUse with task|Task matcher injects the ruleset into subagents. Level switches (/ponytail lite|full|ultra|off) work automatically.

Antigravity CLI

Google is renaming Gemini CLI to Antigravity CLI (the agy binary); the same extension installs there:

agy plugin install https://github.com/DietrichGebert/ponytail

It reuses this repo's gemini-extension.json. One difference: Antigravity converts the /ponytail commands into skills, so you type them into the chat (e.g. /ponytail-review as a message) instead of picking them from a slash menu. Until the migration completes (around June 18, 2026), gemini extensions install still works too. To run it as an always-on rule instead, drop the ruleset into .agents/rules/.

Hermes Agent

hermes plugins install DietrichGebert/ponytail --enable

Restart Hermes after installing. The plugin injects the active Ponytail mode before each LLM turn, registers the bundled skills as ponytail:<skill>, and adds /ponytail, /ponytail-review, /ponytail-audit, /ponytail-debt, /ponytail-gain, and /ponytail-help. In shared gateways, restrict /ponytail to trusted users with Hermes slash-command access controls; runtime mode is process-local.

CodeWhale

Reads AGENTS.md from the project root, zero setup. Copy AGENTS.md to your project, or run codewhale from a checkout of this repo. That's it.

Swival

Stage the collection in your library first, then add the skills you want:

swival skills add --global https://github.com/DietrichGebert/ponytail  # stage into ~/.config/swival/library
swival skills add ponytail