L

lavish

by @kunchenguidv
4.6(120)

This skill transforms complex or visual AI agent responses into rich, reviewable HTML artifacts, allowing users to annotate and provide feedback. Utilizing the lavish-axi CLI tool, it presents plans, comparisons, diagrams, tables, code diffs, or reports in a more intuitive and interactive format. This significantly enhances human-AI collaboration efficiency and the quality of feedback loops.

cli-utilitieshtml-generationfeedback-loopagent-outputGitHub
Installation
npx skills add https://github.com/kunchenguid/lavish-axi --skill lavish
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Before / After Comparison

1 组
Before

Before this skill, users or teams reviewing complex plans, diagrams, or reports generated by AI agents often spent significant time sifting through plain text. It was difficult to quickly grasp key information and potential issues, leading to lengthy and imprecise feedback cycles.

After

This skill transforms AI agent responses into interactive HTML pages, allowing users to directly annotate and provide feedback on visual content. This dramatically reduces the time needed to understand complex information and makes feedback more intuitive and efficient, accelerating decision-making.

SKILL.md

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

  1. Create the HTML artifact (default location .lavish/<name>.html in the working directory).
  2. Run npx -y lavish-axi <html-file> to open or resume a review session in the browser.
  3. Run npx -y lavish-axi poll <html-file> to long-poll for the user's annotations, queued prompts, and browser-reported layout_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.
  4. If poll returns layout_warnings, fix overflow, clipped text, or overlapping unreadable content and re-check before involving the human.
  5. Apply human feedback, then poll again with --agent-reply "<message>" to reply in the browser and keep the loop going.
  6. Run npx -y lavish-axi end <html-file> when the review is finished.

Visual guidance

  • Use visual hierarchy to make the most important decisions, risks, tradeoffs, and next actions obvious at a glance
  • Use visual structure such as sections, cards, tables, diagrams, annotated snippets, and side-by-side comparisons instead of long prose
  • Choose typography, spacing, color, and layout deliberately so the artifact has a clear point of view
  • Prevent horizontal overflow at every nesting level: nested grid/flex children also need minmax(0, 1fr) tracks and min-width: 0, especially when badges, labels, or status text use wide pixel or monospace fonts; wrap, truncate, or contain long unbreakable text deliberately

Playbooks

Run npx -y lavish-axi playbook <id> for focused, detailed guidance on any of these. One artifact often combines several playbooks (for example a plan that includes a comparison and a diagram), so MUST open each matching playbook before writing HTML. For flows, architecture, state, or sequence diagrams, do not hand-build boxes-and-arrows from div/flexbox; open the diagram playbook and use Mermaid unless SVG is needed for richly annotated nodes.

  • diagram - Map relationships, flows, state, and architecture
  • table - Turn dense records into scan-friendly review surfaces
  • comparison - Show options, tradeoffs, and current vs target behavior
  • plan - Explain a product or technical plan before implementation
  • code - Render source code, code files, patches, PR diffs, and before/after code inside Lavish artifacts
  • input - Must be used when the agent needs to collect user input on decisions, choices, preferences, triage, scope, or other structured feedback from within the artifact
  • slides - Create a deliberate presentation when slides are requested

Commands & rules

  • Run npx -y lavish-axi <html-file> to open or resume a Lavish Editor session
  • Unless the user specifies another location, create HTML artifacts in the current working directory under .lavish/
  • Lavish serves the html file through a local express.js server. If your html needs to reference other filesystem assets such as images, CSS, fonts, and local scripts, copy them into the same directory as the HTML file, then reference them with relative paths from that directory. Never prepend / to those asset paths - root paths won't work
  • Run npx -y lavish-axi poll <html-file> to wait for user feedback or browser-reported layout_warnings. It long-polls and stays silent until the user sends feedback, ends the session, or the real browser reports fresh layout_warnings, so leave it running - never kill it. Fix layout_warnings before involving the human. 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
  • Run npx -y lavish-axi end <html-file> to end a session
  • Run npx -y lavish-axi stop to shut down the background server (it also self-stops when idle or after the last session ends with nothing connected)
  • Run npx -y lavish-axi playbook <playbook_id> for focused artifact guidance. One artifact often combines several playbooks (for example a plan that includes a comparison and a diagram), so MUST open each matching playbook before writing HTML.
  • Lavish does not auto-inject any design system - artifacts stay portable so they render identically when opened directly without lavish-axi running. Before writing any HTML, decide the design direction in this strict priority order, and only move to the next step when the current one truly yields nothing: (1) if the user asked for a specific look or named design system, use that; (2) otherwise you must first inspect the project the artifact is about - the subject or product whose content or UI it represents, which may differ from your current working directory - and match that project's design system: Tailwind or theme config, shared CSS variables or design tokens, component library, brand assets, or existing styled pages. If the artifact previews, proposes, or mocks a specific app's UI, render it in that app's own design system so it faithfully shows the product, even when you are running in a different repo; (3) only when both steps come up empty, use the Lavish-recommended Tailwind CSS browser runtime v4 + DaisyUI v5, available via CDN - run npx -y lavish-axi design for a content-to-playbook router, a copy-pasteable CDN snippet, a Mermaid CDN snippet/init for diagrams, and the DaisyUI component reference, and prefer the Tailwind/DaisyUI CDN snippet over hand-writing styles unless explicitly instructed otherwise by the user. When you deliver the artifact, state which of the three design sources you used and why.
  • 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

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Installs22.5K
Rating4.6 / 5.0
Version
Updated2026年10月23日
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Timeline

Created2026年6月25日
Last Updated2026年10月23日
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