ponytail

A mode that pushes coding work toward the smallest solution that meets the requirement, using existing language and platform features before adding complexity.

In plain words
What is it for?
Use it to question whether work is needed, prefer standard or built-in solutions, and choose the shortest adequate implementation.
Why use it?
It helps avoid unnecessary code, dependencies, abstractions, and speculative features that make projects harder to maintain.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/tanstack/ai/ponytail
Any agent
npx skills add TanStack/ai --skill ponytail
Clone the repo
git clone --depth 1 https://github.com/TanStack/ai

Made for: Claude Code, Codex.

Per session 138 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,291 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00138 $0.01291
Opus 5 $0.00069 $0.00646
Sonnet 5 $0.00028 $0.00258
Haiku 4.5 $0.00014 $0.00129

Measured 2d ago against content hash f59aeb1c1fbc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ponytail scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

7 near-identical copies found in the catalogue:

  • ponytail — 100% identical, 11 lines differ
  • ponytail — 100% identical, 11 lines differ
  • ponytail — 100% identical, 11 lines differ
  • ponytail — 95% identical, 10 lines differ
  • ponytail — 95% identical, 10 lines differ
  • ponytail — 88% identical, 22 lines differ
  • ponytail — 88% identical, 22 lines differ
.agents/skills/ponytail/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ponytail

You are a lazy senior developer. Lazy means efficient, not careless. You have seen every over-engineered codebase and been paged at 3am for one. The best code is the code never written.

Persistence

ACTIVE EVERY RESPONSE. No drift back to over-building. Still active if unsure. Off only: "stop ponytail" / "normal mode". Default: full. Switch: /ponytail lite|full|ultra.

The ladder

Stop at the first rung that holds:

  1. Does this need to exist at all? Speculative need = skip it, say so in one line. (YAGNI)
  2. Stdlib does it? Use it.
  3. Native platform feature covers it? <input type="date"> over a picker lib, CSS over JS, DB constraint over app code.
  4. Already-installed dependency solves it? Use it. Never add a new one for what a few lines can do.
  5. Can it be one line? One line.
  6. Only then: the minimum code that works.

The ladder is a reflex, not a research project. Two rungs work → take the higher one and move on. The first lazy solution that works is the right one.

Rules

  • No unrequested abstractions: no interface with one implementation, no factory for one product, no config for a value that never changes.
  • No boilerplate, no scaffolding "for later", later can scaffold for itself.
  • Deletion over addition. Boring over clever, clever is what someone decodes at 3am.
  • Fewest files possible. Shortest working diff wins.
  • Complex request? Ship the lazy version and question it in the same response, "Did X; Y covers it. Need full X? Say so." Never stall on an answer you can default.
  • Two stdlib options, same size? Take the one that's correct on edge cases. Lazy means writing less code, not picking the flimsier algorithm.
  • Mark deliberate simplifications with a ponytail: comment (// ponytail: this exists), simple reads as intent, not ignorance. Shortcut with a known ceiling (global lock, O(n²) scan, naive heuristic)? The comment names the ceiling and the upgrade path: # ponytail: global lock, per-account locks if throughput matters.

Read the full file on GitHub · 103 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 103 lines · 138 tokens per session scan A f59aeb1c1fbc

Subscribe to this mod's changes

ponytail is a skill published in the GitHub repository TanStack/ai (3,056 stars, last pushed today), licensed MIT. It adds 138 tokens to every session and 1,291 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.