review-interface

Name: review-interface
Availability: InStock
Author: kinnerkarmanish

Generate self-contained HTML annotation interfaces for reviewing agent outputs and traces.

Updated Apr 26, 2026

Search Tags:#ui #review #jsonl #annotation #data-labeling #agent-trace

Ready to run

npx skills add https://github.com/kinnerkarmanish/mak --skill review-interface-kinnerkarmanish

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: review-interface
Source: https://github.com/kinnerkarmanish/mak/tree/main/library/skills/workspace/review-interface
Command: npx skills add https://github.com/kinnerkarmanish/mak --skill review-interface-kinnerkarmanish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate self-contained HTML annotation interfaces that enable efficient human review of agent outputs and traces.

Core Features & Use Cases

Self-contained UI: runs entirely in the browser from a single HTML file with no server.
Supports reviewing LLM outputs, agent execution traces, side-by-side comparisons, and data labeling.
Exports labeled results and tracks progress for audit and improvement workflows.

Quick Start

Run /review-interface with your data file to generate the self-contained HTML review interface.

Frequently Asked Questions about review-interface

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create a data labeling interface for reviewing agent traces without setting up a server?▼

You can generate a self-contained HTML annotation interface that runs entirely in the browser from a single file, requiring no server setup. It produces a single HTML file with inline CSS/JS to load JSONL data and review agent traces.

What is the best way to build a UI for human review of LLM outputs?▼

The best way to build a UI for human review of LLM outputs is to use a tool that generates a self-contained HTML file. This interface supports configurable annotation controls, progress tracking, and result exports for quality assessment.

Can I load JSONL data into a custom annotation tool for side-by-side comparisons?▼

Yes, you can load JSONL data into a custom annotation tool for side-by-side comparisons. The interface supports reviewing LLM outputs, agent execution traces, and data labeling tasks directly in the browser.

Does the generated HTML annotation interface support progress tracking and result export?▼

Yes, the generated HTML annotation interface supports both progress tracking and result export. It produces a single HTML file with inline CSS/JS to help you track annotation progress and export labeled results for audits.

How do I audit AI outputs and traces locally without a backend?▼

To audit AI outputs and traces locally without a backend, generate a self-contained HTML annotation interface. It runs entirely in the browser from a single file, loading JSONL data to facilitate quality assessment and audit workflows.