build-review-interface

Render LLM trace data in a browser for Pass/Fail labeling and JSONL export.

Updated Apr 19, 2026
One-click install
npx skills add https://github.com/majidraza1228/eval-framework --skill build-review-interface-majidraza1228
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: build-review-interface
Source: https://github.com/majidraza1228/eval-framework/tree/main/skills/build-review-interface
Command: npx skills add https://github.com/majidraza1228/eval-framework --skill build-review-interface-majidraza1228

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build-review-interface provides a browser-based annotation tool to review LLM traces and collect human labels at scale.

Core Features & Use Cases

  • Render trace data in domain-native formats (Markdown, code, JSON, tables, emails)
  • Capture binary Pass/Fail labels with optional notes and keyboard shortcuts for rapid labeling
  • Export labeled traces as JSONL for integration with eval pipelines and QA workflows

Quick Start

Open the UI in a browser and load your trace JSONL file to start labeling traces.

Frequently Asked Questions about build-review-interface

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

FAQPage Schema
How do I review LLM traces and label data at scale?

Reviewing LLM traces at scale requires a browser-based annotation interface that renders raw JSON traces and tool-call logs. This Skill provides a single-file HTML UI to capture Pass/Fail labels and notes, streamlining human evaluation workflows.

How do I start labeling JSONL trace files in my browser?

To start labeling JSONL trace files, simply open the provided HTML UI in your browser and load your trace file. The interface supports keyboard shortcuts for rapid Pass/Fail labeling and optional notes attachment.

Can I render different data types like Markdown and code when reviewing LLM traces?

Yes, the annotation interface renders trace data in domain-native formats including Markdown, code, JSON, tables, and emails. This allows you to review intermediate steps and tool-call logs in their original context.

Does this data labeling tool require any dependencies or backend setup?

No dependencies or backend setup are required. The tool is a minimal, single-file HTML UI that runs entirely in the browser, allowing you to load trace JSONL files locally and export labeled data without external services.

What is the best way to export labeled LLM traces for eval pipelines?

The best way to export labeled LLM traces for eval pipelines is using the JSONL format. The UI exports your Pass/Fail labels and notes as a JSONL file, ensuring direct integration with QA workflows and evaluation systems.

What are the limitations of using a single-file HTML UI for data labeling?

The single-file HTML UI is limited to local browser processing without server-side storage or collaboration features. It is designed for individual rapid labeling of JSONL traces rather than multi-user annotation platforms.