ShopGuard is a decisive risk-control layer for e-commerce that evaluates merchant credibility and after-sales friction to provide clear buy-or-bypass directives.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install shopguard
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install shopguard using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is ShopGuard?
ShopGuard serves as a sophisticated shopping strategist designed to sit above traditional price comparison tools. Instead of simply asking if a product is worth the price, this skill focuses on whether a specific purchase route is safe. It analyzes the merchant trust layer, potential refund difficulty, and hidden post-purchase friction across major Chinese marketplaces like Taobao, JD, and PDD.
By utilizing Openclaw Skills, ShopGuard converts complex risk signals into actionable verdicts. It moves beyond generic risk scores to tell users exactly when to switch sellers, what evidence to retain for protection, and whether a low-price option is actually a long-term hassle trap. It is particularly essential for high-stakes purchases involving gifts, electronics, or urgent delivery needs.
ShopGuard Use Cases
- Evaluating if a significantly cheaper seller is a safe choice or an after-sales risk.
- Determining if a merchant is reliable enough for high-stakes items like gifts or urgent electronics.
- Identifying exactly what evidence (screenshots, terms) to save before placing an order to maintain leverage.
- Comparing different seller types (Flagship vs. Third-Party) to find the best balance of price and protection.
- Forecasting how difficult a refund or return process will be based on platform and seller heuristics.
How ShopGuard Works
- The skill assesses the user's stakes, such as whether the item is for self-use, a gift, or an urgent requirement.
- It normalizes the seller path by identifying if the merchant is an official flagship, an authorized dealer, or an unverified third-party.
- Public warning signals are scanned, including price gaps, warranty clarity, and review patterns concerning packaging or delivery.
- It forecasts potential after-sales friction, calculating the proof burden and return logistics difficulty.
- A final verdict is generated, providing a clear action: buy, switch seller, switch platform, or avoid entirely.
- The skill provides a specific evidence checklist for the user to capture before and after delivery.
ShopGuard Setup
To integrate ShopGuard into your workflow, ensure your environment supports Openclaw Skills and follow these steps:
# Install the ShopGuard skill via the clawdbot CLI
clawdbot install shopguard
# Verify the installation and version
shopguard --version
ShopGuard Data Schema & Taxonomy
ShopGuard organizes data through a multi-layered trust taxonomy to provide structured risk assessments:
| Data Layer | Description |
|---|---|
| Stakes Context | Categorizes intent (Gifting, Urgency, Self-use) to weight risk sensitivity. |
| Seller Tier | Ranks merchants from Official Flagship (High Trust) to Unclear-source (High Risk). |
| Risk Signals | Tracks title vagueness, SKU inconsistencies, and historical refund friction patterns. |
| Evidence Manifest | Lists required captures: product page, store badges, and unboxing video requirements. |
ShopGuard Advanced Features
- Multi-platform comparison hooks to pressure-test low-price routes found by other shopping tools.
- Automated evidence checklists tailored to specific product categories like skincare or electronics.
- Decision-triage logic that differentiates between product-specific risks and seller-specific risks.
- Live browser workflow for real-time inspection of seller badges, return promises, and review patterns.
- Scenario-based conservative biasing for high-ticket or authenticity-sensitive items.
SKILL.md
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