Ponytail is a set of instructions and extensions that guides AI coding agents toward smaller, simpler code changes while retaining safety checks. It is intended for developers using agents such as Claude Code, and the catalogue entries are its skills, instructions, plugin, and rule.
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.
git clone --depth 1 https://github.com/DietrichGebert/ponytailWrote 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/commands/dietrichgebert/ponytail/ponytail-gain)<a href="https://agentmods.dev/commands/dietrichgebert/ponytail/ponytail-gain"><img src="https://agentmods.dev/badge/commands/dietrichgebert/ponytail/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/commands/dietrichgebert/ponytail/ponytail-gain"><img src="https://agentmods.dev/badge/commands/dietrichgebert/ponytail/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 | $0.00015 | $0.00321 |
| Opus 5.5 | $0.00006 | $0.00128 |
| Sonnet 5.5 | $0.00003 | $0.00064 |
| Haiku 4.5 | $0.00002 | $0.00032 |
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 17d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- ponytail-gain — 100% identical, 4 lines differ
- ponytail-gain — 100% identical, 2 lines differ
- ponytail-gain — 92% identical, 4 lines differ
What it actually says
Show the ponytail gain scoreboard. One shot, change nothing: do not switch mode, write flag files, or persist anything. Render the published agentic benchmark of Ponytail 5 (39 tasks on a real FastAPI + React repo plus bug fixes, security and privacy cases and small apps, Opus 5.5, 5 runs each, against the same agent without the skill; geometric mean of per-task medians; source benchmarks/results/2026-10-07-agentic.md and the README) as plain ASCII bars, ponytail as a share of the no-skill baseline (100%): Lines of code 47% (down 53%); Output tokens 55% (down 45%); Cost 74% (down 26%); Time 59% (down 41%); Hidden checks passed 97% (no-skill 96%; 18 of the tasks have hidden checks for correctness and safety); Tests where the logic needs one 98% (no-skill 68%). The bar length shows the share, the label carries the exact figure. These are benchmark averages, not this repo. NEVER print a per-repo savings number: the unbuilt version was never written, so there is no real baseline to subtract from in a live repo. For real per-repo figures, point to /ponytail-debt (the counted shortcut ledger) and /ponytail-audit (what is still cuttable). Report only.
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.
- 17d ago Changed fb3eae02ae26
- 20d ago Changed 0818122e35e2
- 1mo ago First seen · 6 lines · 15 tokens per session scan A 33514a67319e
ponytail-gain is a command published in the GitHub repository DietrichGebert/ponytail (176,199 stars, last pushed 8d ago), licensed MIT. It adds 15 tokens to every session and 321 once invoked, about $0.0001 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 2026-09-08.
Other commands, from other repositories
archetype-impact
Scan the current workspace for all references to a given archetype across templates (.oet / .t.json source, .opt compiled) and AQL files, producing an impact table useful before editing a widely-reused archetype.
openehr-explain
One-stop router that explains or looks up any openEHR thing — auto-detects an archetype, a template, an RM/AM/BASE type, an RM structural concept, an ADL idiom, an AQL query or keyword, or a terminology code (replaces /archetype-explain, /template-explain, /type-spec, /rm-structure, /adl-idiom, /terminology).
ckm-search
Search the openEHR Clinical Knowledge Manager (CKM) for archetypes or templates.
deps-audit
Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.
learn
Force claude-smart to extract learnings from this session now.
prototype
You are building a proof-of-concept for the current Grainulator sprint. Read CLAUDE.md for sprint context and claims.json for existing research claims.