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/Cyvid7-Darus10/claude-code-configWrote 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/cyvid7-darus10/claude-code-config/ponytail-gain)<a href="https://agentmods.dev/commands/cyvid7-darus10/claude-code-config/ponytail-gain"><img src="https://agentmods.dev/badge/commands/cyvid7-darus10/claude-code-config/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/cyvid7-darus10/claude-code-config/ponytail-gain"><img src="https://agentmods.dev/badge/commands/cyvid7-darus10/claude-code-config/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 | $0.00233 |
| Opus 5.5 | $0.00006 | $0.00093 |
| Sonnet 5.5 | $0.00003 | $0.00047 |
| Haiku 4.5 | $0.00002 | $0.00023 |
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.
This is a copy
100% identical to ponytail-gain — 2 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.
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 benchmark medians (5 everyday tasks; models Haiku, Sonnet, Opus; source benchmarks/ and the README) as plain ASCII bars: Lines of code, no-skill 100% vs ponytail 6-20% (down 80-94%); Cost, no-skill 100% vs ponytail 23-53% (down 47-77%); Speed, ponytail 3-6x faster. The bar length shows the measured range, the label carries the exact figure. These are benchmark medians, 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.
- 1mo ago First seen · 6 lines · 15 tokens per session scan A 33514a67319e
ponytail-gain is a command published in the GitHub repository Cyvid7-Darus10/claude-code-config (2 stars, last pushed 3mo ago), licensed MIT. It adds 15 tokens to every session and 233 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ponytail-gain, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
datadog
Quick reference for Datadog CLI commands.
prepare-dataset
Analyse a raw data file (CSV, JSON, JSONL, TSV, Parquet) and generate a complete Python script scripts/dataset/ .py that inherits from BaseDatasetPreparer and transforms the file into a JSONL dataset ready for fine-tuning with Unsloth.
worktree.delete
Delete a git worktree under .worktree/ or clean up all merged worktrees.
worktree.create
Create a git worktree under .worktree/ and relocate the OpenCode session there.
worktree
Manage git worktrees under .worktree/ (create, list, delete, clean-merged).
speckit.polish
Polish and complete speckit workflow — quality gate, docs, changelog, MR.