Skills for Real Engineers. Straight from my .agents directory.
build-review-interface
Agent skill fromai-evals-course/evals-skills
Build a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.
How to install build-review-interface
npx skills add ai-evals-course/evals-skills# or add only this skill to Claude Code: copy the folder
skills/build-review-interface/
# into
~/.claude/skills/build-review-interface/ (all projects)
.claude/skills/build-review-interface/ (one project)
1# Build a Custom Annotation Interface23## Overview45Build an HTML page that loads traces from a data source (JSON/CSV file), displays one trace at a time with Pass/Fail buttons, a free-text notes field, and Next/Previous navigation. Save labels to a local file (CSV/SQLite/JSON). Then customize to the domain using the guidelines below.67## Data Display89Format all data in the most human-readable representation for the domain. Emails should look like emails. Code should have syntax highlighting. Markdown should be rendered. Tables should be tables. JSON should be pretty-printed and collapsible.1011- **Collapse repetitive elements.** If every trace shares the same system prompt, put it in a `<details>` toggle.12- **Extract and surface key metadata.** If traces contain a property name, client type, or session ID buried in the data, extract it and display it prominently as a header or badge.13- **Color-code by role or status.** Use left-border colors to distinguish user messages, assistant messages, tool calls, and system prompts at a glance.14- **Group related elements visually.** Tool calls and their responses should be visually linked (indentation, shared border).15- **Collapse what doesn't help judgment.** Verbose tool response JSON, intermediate reasoning steps, and debugging context go behind toggles.16…
Excerpt shown under the repository's Apache-2.0 license.
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