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DietrichGebert/ponytail

ponytail

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.

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What it does

Ponytail is a rule set and skill collection that pushes an AI coding agent to write as little code as a task needs. It ships as a plugin for Claude Code, Codex, GitHub Copilot CLI, Gemini CLI and several other agent hosts, and as plain rules files for editors such as Cursor, Windsurf and Cline.

Before writing anything, the agent works down a fixed ladder: drop the feature if it does not need to exist, reuse what the codebase already has, then try the standard library, a native platform feature, an installed dependency and a one-line answer. New code comes only after all of those fail. The README is firm that validation, error handling, security and accessibility are off limits for cutting. Its example: asked for a date picker, the agent reaches for a native <input type="date"> instead of adding a library and a wrapper component.

The plugin adds commands to set the intensity (lite, full, ultra or off), to review a diff or a whole repository for over-engineering, and to collect deferred shortcuts into a ledger. The maintainer publishes a benchmark: across twelve feature tasks on a FastAPI and React template with Haiku 4.5, the README reports about 54% fewer lines of code than the same agent without the skill, plus smaller drops in tokens, cost and time.

Why it matters

Coding agents often over-build, pulling in dependencies and abstractions a task never needed. The obvious fix is a short prompt telling the agent to keep things minimal, and Ponytail's benchmark includes exactly that as a comparison. According to the README, the plain prompt let one safety case slip in adversarial tests while Ponytail passed all of them. The project also says where the effect fades: on code that is already small, the reduction is close to zero.

Main features

  • Seven-step ladder the agent checks before writing new code
  • Four levels (lite, full, ultra, off) switched with /ponytail
  • /ponytail-review returns a delete-list for the current diff
  • /ponytail-audit looks for over-engineering across the whole repository
  • /ponytail-debt collects deferred shortcuts into a ledger
  • Plugins for Claude Code, Codex, Copilot CLI, Gemini CLI, OpenCode, Hermes Agent and Devin CLI
  • Copyable rules files for Cursor, Windsurf, Cline, Kiro and Qoder
  • Default level set through PONYTAIL_DEFAULT_MODE or an optional config file

Who it's for

  • Developers on Claude Code, Codex or Copilot CLI whose agent adds code and packages nobody asked for
  • Teams that want a repeatable over-engineering check on a diff or an entire repository
  • Caveman users who want the same restraint applied to the code, since the README says the two combine

Strengths

  • Publishes its benchmark method and per-task results, and replaced its earlier 80-94% single-shot figure after GitHub issue #126 showed the baseline was unfair
  • Keeps validation, error handling, security and accessibility outside what it will trim
  • Runs in a wide range of agents, from full plugins to instruction-only rules files
  • Needs no config file and is MIT licensed

Limitations

  • The headline numbers are the maintainer's own, from one repository, one model (Haiku 4.5) and four runs per task
  • The README notes that on GPT-5.5 cost and latency can rise instead of fall
  • Instruction-only setups such as the Cursor rule file, Windsurf, Cline, Copilot and Kiro get the ruleset without the commands
  • With the Cursor hooks install, subagents and cloud agents run without the ruleset

Getting started

Setup depends on the agent. Claude Code, Codex, Copilot CLI, Gemini CLI and several others install Ponytail as a plugin or extension straight from the GitHub repository, while editors such as Cursor, Windsurf and Cline use a copied rules file; the README lists the exact steps for each host.

Requirements: A supported AI coding agent; the Claude Code and Codex plugins and the Cursor hooks also need Node.js on the PATH.

Always follow the official instructions in the README on GitHub. This overview is our summary, not a copy.

Sources