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.
npx agentmods add skills/diegosouzapw/omniroute/ponytailnpx skills add diegosouzapw/OmniRoute --skill ponytailgit clone --depth 1 https://github.com/diegosouzapw/OmniRouteWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00129 | $0.01775 |
| Opus 5 | $0.00064 | $0.00888 |
| Sonnet 5 | $0.00026 | $0.00355 |
| Haiku 4.5 | $0.00013 | $0.00178 |
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 3d 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.
This is a copy
72% identical to ponytail — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: ponytail description: > Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes). argument-hint: "[lite|full|ultra]" license: MIT
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:
- Does this need to exist at all? Speculative need = skip it, say so in one line. (YAGNI)
- Already in this codebase? A helper, util, type, or pattern that already lives here → reuse it. Look before you write; re-implementing what's a few files over is the most common slop.
- Stdlib does it? Use it.
- Native platform feature covers it?
<input type="date">over a picker lib, CSS over JS, DB constraint over app code. - Already-installed dependency solves it? Use it. Never add a new one for what a few lines can do.
- Can it be one line? One line.
- Only then: the minimum code that works.
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.
- 3d ago First seen · 130 lines · 129 tokens per session scan A cb36c9f060ac
ponytail is a skill published in the GitHub repository diegosouzapw/OmniRoute (59,841 stars, last pushed yesterday), licensed MIT. It adds 129 tokens to every session and 1,775 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 72% identical to ponytail, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
workflow-ai-coding
Edit, validate, debug, publish, and inspect ReachAI Workflow drafts through the Workflow AI Coding REST API. Use when asked to create or modify a workflow graph, add/update/delete nodes or edges, validate GraphSpec, dry-run or debug-run a workflow, inspect trace/run output, check release readiness, publish a validated…
reachai-onboarding
Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure…
9router-web-fetch
Fetch URL → markdown / text / HTML via 9Router /v1/web/fetch using Firecrawl / Jina Reader / Tavily Extract / Exa Contents. Use when the user wants to scrape a webpage, extract URL content, read article, or convert a URL to markdown.
9router-web-search
Web and X search via 9Router /v1/search using Tavily / Exa / Brave / Serper / SearXNG / Google PSE / Linkup / SearchAPI / You.com / Perplexity / Xquik. Use when the user wants to search the web, find articles, or search public X posts.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
9router-stt
Speech-to-text via 9Router /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio files.