Agent skill · Audited from source

build-review-interface

skill · hamelsmu/evals-skillsMIT licenceAudited 20 Oct 2026 · source 4aa6c7c220dd

★ 1,677pushed 65 days agoarchived repository
✓SAFEGrade B · 89/100 · 20 Oct 2026 · 4aa6c7c
1,677repo stars · whole hamelsmu/evals-skills

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 4aa6c7c220dd and found no filesystem, network or shell access and no hard-coded secrets. What it does: 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.

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Cite this page

ModelsAtlas. “build-review-interface (hamelsmu/evals-skills)”. Retrieved 20 Oct 2026. https://modelsatlas.com/skills/hamelsmu/build-review-interface

Trust audit

Audit 0.4.1 · source read 20 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 13,985 of 14,816 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha 4aa6c7c220dd · 20 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

  • Publisherhamelsmu GitHub userOwner of the repository this skill is read from
  • Source folderhamelsmu/evals-skills/skills/build-review-interfaceThe exact folder we audited
  • LicenceMITAs the repository states it
  • Commit pinned for this verdict4aa6c7c220ddA new audit runs when the source changes
  • Repository statusArchivedLast push 65 days ago
  • Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

What it tells the agent

Full source on GitHub ↗

Its description: 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.

---
name: build-review-interface
description: >
  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.
---

# Build a Custom Annotation Interface

## Overview

Build 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.

## Data Display

Format 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.

- **Collapse repetitive elements.** If every trace shares the same system prompt, put it in a `<details>` toggle.
- **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.
- **Color-code by role or status.** Use left-border colors to distinguish user messages, assistant messages, tool calls, and system prompts at a glance.
- **Group related elements visually.** Tool calls and their responses should be visually linked (indentation, shared border).
- **Collapse what doesn't help judgment.** Verbose tool response JSON, intermediate reasoning steps, and debugging context go behind toggles.
- **Highlight what matters most.** Make the primary content reviewers judge visually dominant. Bold key entities (prices, dates, names). Use font size and spacing to create hierarchy.
- **Show the full trace.** Include all intermediate steps (tool calls, retrieved context, reasoning), not just the final output. Collapse them by default but keep them accessible.
- **Sanitize rendered content.** Strip raw HTML from LLM outputs before rendering. Disable images in rendered markdown if they could be tracking pixels.

## Feedback Collection

Annotate at the trace level. The reviewer judges the whole trace, not individual spans.

- Binary Pass/Fail buttons as the primary action.
- Free-text notes field for the reviewer to describe what went wrong (or right).
- Defer button for uncertain cases.
- Auto-save on every action.

Once you have established failure categories from error analysis, you can later add predefined failure mode tags as clickable checkboxes, dropdowns or picklists so reviewers can select from known categories in addition to writing notes.  But don't add these in the initial build.

## Navigation and Status

- Next/Previous buttons and keyboard arrow keys.
- Trace counter showing position and progress ("12 of 87 remaining").
- Jump to specific trace by ID.
- Counts of labeled vs unlabeled traces.

## Keyboard Shortcuts

```
Arrow keys = Navigate traces
1 = Pass              2 = Fail
D = Defer             U = Undo last action
Cmd+S = Save          Cmd+Enter = Save and next
```

## Selecting Traces to Load

Build the app to accept traces from any source (JSON/CSV file). Keep sampling logic outside the app in a separate script. Start with random sampling.

## Additional Features

**Reference panel:** Toggle-able panel showing ground truth, expected answers, or rubric definitions alongside the trace.

**Filtering:** Filter traces by metadata dimensions relevant to the product (channel, user type, pipeline version).

**Clustering:** Group traces by metadata or semantic similarity. Show representative traces per cluster with drill-down.

## Design Checklist

- [ ] Same layout, controls, and terminology on every trace
- [ ] Pass and Fail buttons are visually distinct (color, size)
- [ ] Keyboard shortcuts work for all primary actions
- [ ] Full trace accessible even when sections are collapsed
- [ ] Labels persist automatically without explicit save
- [ ] Trace-level annotation (not span-level) as the default
- [ ] All data rendered in its native format (markdown as HTML, code with highlighting, JSON pretty-printed, tables as HTML tables, URLs as clickable links)

## Testing

After building the interface, verify it with Playwright.

**Visual review:** Take screenshots of the interface with representative trace data loaded. Review each screenshot for:
- Layout and spacing: is the visual hierarchy clear? Can you immediately see what matters?
- Readability: is all data rendered in its native format? Are there any raw JSON blobs, unrendered markdown, or unstyled content?
- Aesthetics: does the interface look professional and clean? Would a domain expert use this?
- Responsiveness: does the layout hold at different window sizes?

**Functional test:** Write a Playwright script that performs a full annotation workflow:
1. Load the app and verify traces are displayed
2. Click Pass on a trace, verify the label is saved
3. Click Fail on a trace, add a note, verify both are saved
4. Click Defer, verify it is recorded
5. Navigate forward and backward with buttons and keyboard shortcuts
6. Verify the trace counter updates correctly
7. Verify auto-save by reloading the page and checking labels persist
8. Expand collapsed sections (system prompts, tool calls) and verify content is accessible
9. Test that all keyboard shortcuts trigger the correct actions

Excerpt, first 5,408 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit 4aa6c7c

Download folder ↗
git clone https://github.com/hamelsmu/evals-skills
git -C evals-skills checkout 4aa6c7c220dd

The author documents no install command, so this uses the repository directly. Our verdict covers commit 4aa6c7c220dd only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 4aa6c7c220dd

README badge

For maintainers · shows the latest audit

ModelsAtlas: SAFE BMODELSATLASSAFE · B
[![ModelsAtlas: SAFE B](https://modelsatlas.com/skills/hamelsmu/build-review-interface/badge.svg)](https://modelsatlas.com/skills/hamelsmu/build-review-interface/)

The badge shows our verdict and grade for this skill, and links back to this page. It changes when the audit does.

Add this badge to your README

[![ModelsAtlas: SAFE B](https://modelsatlas.com/skills/hamelsmu/build-review-interface/badge.svg)](https://modelsatlas.com/skills/hamelsmu/build-review-interface/)

Questions about build-review-interface

What does the build-review-interface skill do?
Skills for AI Evals to compliment the course: AI Evals For Engineers & PMs
Is build-review-interface safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can build-review-interface access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (4aa6c7c220dd), read on 2026-10-20. The repository is watched, and a new audit runs when it changes — this is the first audit.

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