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What is lavish-axi?

kunchenguid/lavish-axi — explained in plain English

Analysis updated 2026-05-18

118JavaScriptAudience · developerComplexity · 1/5Setup · easy

In one sentence

A local command line tool that lets you click and annotate AI generated HTML files in a browser, then sends structured feedback back to the agent.

Mindmap

mindmap
  root((Lavish))
    What it does
      Annotate HTML in browser
      Structured feedback to agent
      Local only
    Tech stack
      Node.js CLI
      npm package
    Use cases
      Agent HTML review
      Diagrams and slide decks
      Interactive forms
    Audience
      AI agents
      Developers

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Give an AI agent precise, element level feedback on an HTML artifact instead of screenshots.

USE CASE 2

Review AI generated diagrams, tables, or slide decks in a browser and request specific changes.

USE CASE 3

Let an AI agent open a feedback session on a file and poll for your annotations.

USE CASE 4

Use prebuilt playbooks to help an agent produce better HTML output for common artifact types.

What is it built with?

Node.jsJavaScriptCLI

How does it compare?

kunchenguid/lavish-axifastify/fastify-schedule863401402/image-provenance
Stars118119124
LanguageJavaScriptJavaScriptJavaScript
Last pushed2026-07-01
MaintenanceActive
Setup difficultyeasyeasyeasy
Complexity1/52/51/5
Audiencedeveloperdevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

So what is it?

Lavish is a command line tool built as a richer editor for HTML files that AI coding agents generate. The problem it addresses: when an AI assistant produces an HTML file, such as a web page, a plan, or an interactive document, the usual way to give feedback is to take a screenshot and describe the change in a text message. That approach throws away the interactivity that makes HTML useful in the first place. Lavish opens the generated HTML file in a local browser window and layers annotation tools on top of it. You can click specific elements on the page, highlight ranges of text, or type feedback into a chat panel, and that feedback is turned into a structured prompt that tells the agent exactly what to change and where. The agent updates the file, and the browser view reloads automatically to show the result. Everything runs on your own machine. There is no cloud service involved, and the HTML file itself is never modified in a special way, so it still looks and works normally if you open it directly in any browser outside of Lavish. The tool is designed to be called directly by AI agents rather than by a human typing commands. The project describes this pattern as an AXI, a command line tool built specifically for agent use, meaning an agent can open a session pointed at a file path, poll for human feedback, and receive the annotated instructions back without any special setup or training. Lavish also ships prebuilt playbooks that guide agents toward producing good results for common artifact types, including diagrams, tables, slide decks, comparison views, and interactive forms. It installs through npm and works on macOS, Linux, and Windows.

Copy-paste prompts

Prompt 1
Use npx lavish-axi to open this HTML file so I can annotate what needs to change.
Prompt 2
Install lavish-axi and show me how an agent should call it to get my feedback.
Prompt 3
Explain how Lavish's playbooks help with generating a comparison table artifact.
Prompt 4
Set up Lavish for local use with zero cloud dependency.

Frequently asked questions

What is lavish-axi?

A local command line tool that lets you click and annotate AI generated HTML files in a browser, then sends structured feedback back to the agent.

What language is lavish-axi written in?

Mainly JavaScript. The stack also includes Node.js, JavaScript, CLI.

How hard is lavish-axi to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is lavish-axi for?

Mainly developer.

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