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 skills add dmae97/omk --skill ponytail-gaingit clone --depth 1 https://github.com/dmae97/omkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dmae97/omk/ponytail-gain)<a href="https://agentmods.dev/skills/dmae97/omk/ponytail-gain"><img src="https://agentmods.dev/badge/skills/dmae97/omk/ponytail-gain/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dmae97/omk/ponytail-gain"><img src="https://agentmods.dev/badge/skills/dmae97/omk/ponytail-gain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.01113 |
| Opus 5.5 | $0.00033 | $0.00445 |
| Sonnet 5.5 | $0.00017 | $0.00223 |
| Haiku 4.5 | $0.00008 | $0.00111 |
Grade A, and why
ponytail-gain 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ponytail Gain
Display this scoreboard when invoked. One-shot: do NOT change mode, write flag files, or persist anything.
The figures are the published benchmark medians (5 everyday tasks: email
validator, debounce, CSV sum, countdown timer, rate limiter; three models:
Haiku, Sonnet, Opus). They are measured, not computed from the current repo.
Source: benchmarks/ and the README.
Scoreboard
Render plain ASCII bars. The bar length shows the measured range; the label carries the exact figure:
ponytail gain benchmark median · 5 tasks · 3 models
Lines of code no-skill ████████████████████ 100%
ponytail ██▌················· 6–20% ▼ 80–94%
Cost no-skill ████████████████████ 100%
ponytail █████▌·············· 23–53% ▼ 47–77%
Speed ponytail ▸ 3–6× faster
This repo: /ponytail-debt (shortcuts you deferred)
/ponytail-audit (what's still cuttable)
Honesty boundary
These are benchmark medians, not this repo. NEVER print a per-repo savings
number ("you saved X lines/tokens here"): the unbuilt version was never
written, so there is no real baseline to subtract from in a live repo. The
only real per-repo figures come from /ponytail-debt (a counted ledger), and
this card points there instead of inventing one.
Boundaries
One-shot display. Edits nothing, changes no mode. "stop ponytail" or "normal mode": revert.
OMK Vendoring Notice
This skill is vendored from the upstream Ponytail
project (npm: @dietrichgebert/ponytail) as a pure markdown OMK skill, copied unmodified in
substance from skills/ponytail-gain/SKILL.md at pinned commit
40e50d9
(full SHA 40e50d9e03242aa5dd53ac771950f9127362b25f).
- License: MIT. Copyright (c) 2026 DietrichGebert. Full text:
../ponytail/LICENSE(this repo's vendored copy, kept alongside the primaryponytailskill) and the upstreamLICENSEfile at the pinned commit. - Scope: only the six
skills/*/SKILL.mdfiles were vendored. Upstream's Node/Python extension code (pi-extension/,hooks/*.js,ponytail-mcp/), the Hermes Agent plugin (__init__.py,plugin.yaml), command/hook manifests, and marketplace/CI tooling are not included in OMK. Nothing in this skill executes code; it is inert markdown loaded into the system prompt. Note in particular: the benchmark medians in this scoreboard were measured by the upstream project's ownbenchmarks/harness, which is not vendored here and was not re-run by this integration. - Invocation in OMK: use
/skill:ponytail-gainor!skill:ponytail-gain(shorthand!ponytail-gainalso works when unambiguous), perpackages/coding-agent/docs/skills.md. The bare/ponytail-gainslash form referenced above describes the upstream multi-host package's own command surface (Claude Code, Codex, OpenCode, etc.) and is not an OMK command. - Updates: this is a static vendored copy, not a live package install. To pick up upstream changes, re-vendor against a newer pinned commit; there is no auto-update mechanism here.
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.
- 1mo ago First seen · 85 lines · 83 tokens per session scan A e3073cef2a4f
ponytail-gain is a skill published in the GitHub repository dmae97/omk (148 stars, last pushed today), licensed MIT. It adds 83 tokens to every session and 1,113 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
cool-workflow
Use when the user asks for Cool Workflow, CW, agent workflow control-plane, TypeScript workflow orchestration, phased multi-agent work, background workflow tasks, reusable workflow apps, or auditable agent run state.
batch
Execute batch operations on multiple files in parallel. Automatically discovers files, splits into chunks, and processes with parallel worker agents. Use /batch followed by operation and file pattern. For many independent single-turn transforms (translate/rewrite/extract each file into a new file) that can wait…
batch-api
Prepare a many-file, single-turn transform (translate, rewrite, extract) as a plan and submit it to the asynchronous, half-price DashScope Batch API; results are delivered as new files hours later. Invoke explicitly with /batch-api.
loop
Create a loop that runs a prompt now and follows up either on a fixed schedule or through self-paced wakeups. Usage - /loop check the build, /loop 5m check the build, /loop check the PR every 30m. /loop list to show jobs, /loop clear to cancel all.
gmail
Search, read, draft, and send Gmail when the user asks to work with their inbox or email.
google-calendar
Read schedules, check availability, and create or update Google Calendar events when the user asks to work with their calendar.