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/muratgur/ordinus/lavishnpx skills add muratgur/ordinus --skill lavishgit clone --depth 1 https://github.com/muratgur/ordinusWrote 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/muratgur/ordinus/lavish)<a href="https://agentmods.dev/skills/muratgur/ordinus/lavish"><img src="https://agentmods.dev/badge/skills/muratgur/ordinus/lavish.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.01618 |
| Opus 5 | $0.00030 | $0.00809 |
| Sonnet 5 | $0.00012 | $0.00324 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
lavish 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 6d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lavish Editor
Lavish Editor helps agents turn rich HTML artifacts into collaborative human review surfaces. Whenever you are about to give user a complex response that will be easier to understand via a rich / interactive page, consider using Lavish Editor. First generate an interactive HTML artifact according to user request, then run npx -y lavish-axi <html-file> so the user can visually review it, annotate elements or selected text, queue prompts, and send feedback back through npx -y lavish-axi poll.
You do not need lavish-axi installed globally - invoke it with npx -y lavish-axi <html-file>.
If lavish-axi output shows a follow-up command starting with lavish-axi, run it as npx -y lavish-axi ... instead.
Request
$ARGUMENTS
If the request above is non-empty, the user invoked /lavish explicitly - build an HTML artifact for that request now, following the workflow below.
If it is empty, infer what to visualize from the conversation.
When to use
Use lavish-axi when the user asks for a visual artifact, HTML explainer, interactive prototype, review surface, product or technical plan, comparison, report, or browser-based feedback loop
Workflow
- Create the HTML artifact (default location
.lavish/<name>.htmlin the working directory). - Run
npx -y lavish-axi <html-file>to open or resume a review session in the browser. - Run
npx -y lavish-axi poll <html-file>to long-poll for the user's annotations, queued prompts, and browser-reportedlayout_warnings. The poll stays silent until the user acts or the real browser reports fresh layout warnings - leave it running, never kill it. If your harness limits how long a foreground command may run, run the poll as a background task; if it gets killed or times out anyway, just re-run it - queued feedback is never lost. - If poll returns
layout_warnings, fix overflow, clipped text, or overlapping unreadable content and re-check before involving the human. - Apply human feedback, then poll again with
--agent-reply "<message>"to reply in the browser and keep the loop going. - Run
npx -y lavish-axi end <html-file>when the review is finished.
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.
- 6d ago First seen · 72 lines · 61 tokens per session scan A d2c99f37344b
lavish is a skill published in the GitHub repository muratgur/ordinus (110 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 1,618 once invoked, about $0.0003 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.
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