Atlas / Skills / zjunlp / Webshop Option Selector

Webshop Option SelectorSAFE

skills/zjunlp/webshop-option-selector

Create, Evaluate, and Connect AI Skills

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
1,402
01

Overview

Create, Evaluate, and Connect AI Skills

Read from source at commit 4501e739125cOBSERVED · 2026-10-12
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: webshop-option-selector
description: This skill selects product options or variants (such as size, color, or quantity) on a product detail page based on user preferences or cost optimization. It should be triggered when a product page displays configurable options. The skill evaluates available choices (e.g., 1pcs vs 2pcs) and selects the one that best aligns with the user's constraints, such as minimizing cost.
---
# Skill: WebShop Option Selector

## Trigger Condition
Activate this skill when the observation from the WebShop environment contains:
1. A product detail page (indicated by the presence of a "Price:" field and product title).
2. One or more configurable option categories (e.g., "size:", "color:", "quantity:") with clickable values listed beneath them.

## Core Logic
1. **Parse the Observation:** Identify all configurable option categories and their available values from the current page state.
2. **Apply Selection Strategy:** By default, implement a **cost-minimization strategy**. For each option category, select the value that results in the lowest final price while still meeting any explicit user constraints from the original instruction (e.g., "price lower than 40.00 dollars").
3. **Execute Selection:** Perform a `click[value]` action for the chosen option value. If multiple categories exist, they should be selected in the order they appear on the page.

## Important Notes
- The skill assumes the displayed price updates (or is representative) after an option is selected. Your action should be based on the currently displayed information.
- If the user's instruction contains specific preferences (e.g., "large size", "blue color"), prioritize those over cost minimization.
- If no clear cost difference can be inferred, select the first available option.
- After selecting the desired option, you may proceed with other actions (e.g., `click[buy now]`).

## Example from Trajectory
**Observation Excerpt:**
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-12 · audit v0.4.1 · source sha 4501e739125cfull audit observations/trust-audit/skill/zjunlp__webshop-option-selector.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-124501e739125cSAFEB89first audit
05

Questions

What does the Webshop Option Selector skill do?

Create, Evaluate, and Connect AI Skills

Is Webshop Option Selector 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 Webshop Option Selector 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 (4501e739125c), read on 2026-10-12. The repository is watched, and a new audit runs when it changes — this is the first audit.

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