An intelligent decision-engine designed to guide users through rigorous product research, budget alignment, and ethical purchase validation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install shopping
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 shopping 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 Shopping Assistant?
The Shopping skill is a strategic framework for Openclaw Skills that transforms the way users interact with the marketplace. Instead of impulsive buying, this skill enforces a disciplined approach to consumerism by prioritizing budget constraints, specific use cases, and long-term value over temporary trends. It provides a structured methodology to filter out noise, identify authentic feedback, and verify technical specifications before a purchase is made.
By leveraging this tool within Openclaw Skills, users can automate the vetting process for electronics, furniture, and subscriptions. It integrates deep research methodologies—such as filtering for authentic reviews on forums and cross-referencing historical pricing—to ensure every recommendation is backed by technical truth and financial logic. The skill serves as a preventative measure against poor investments and manufactured marketing urgency.
Shopping Assistant Use Cases
- Determining the optimal budget for complex electronic purchases based on professional vs. casual use.
- Identifying fake reviews and common red flags on major e-commerce platforms to avoid low-quality goods.
- Strategic timing for purchases by analyzing product refresh cycles and upcoming major sales events.
- Deciding when to opt for refurbished or open-box items to maximize value without compromising quality.
- Resolving indecision between competing products using a multi-factor structured comparison framework.
How Shopping Assistant Works
- The skill initiates by prompting for budget, specific use case, and past ownership to establish a technical baseline.
- It conducts multi-source research, prioritizing long-term durability reports and community-driven feedback over sponsored content.
- A price history check is performed via external tracking logic to verify if a sale price represents genuine value.
- The system applies a 'When to Suggest Not Buying' logic to filter out emotional impulses or marginal upgrades.
- A final, single recommendation is generated with clear technical reasoning, prioritizing items with favorable return policies.
Shopping Assistant Setup
To integrate this capability into your environment, ensure your agent is configured to use Openclaw Skills. Run the following commands to initialize the module:
# Register the shopping skill within your local environment
openclaw-cli add skill shopping
# Configure preferences for currency and regional market data
openclaw-cli config shopping --currency USD --region US
Once installed, the assistant will automatically trigger research workflows whenever a purchase-related query is detected.
Shopping Assistant Data Schema & Taxonomy
The Shopping skill for Openclaw Skills organizes decision data using a structured taxonomy to ensure transparency and repeatability.
| Data Point | Description |
|---|---|
| Must-haves | Non-negotiable technical requirements and essential features |
| Nice-to-haves | Bonus attributes that provide incremental value but are not critical |
| Deal-breakers | Specific factors or technical flaws that would trigger a return |
| Total Cost | The full financial impact, including accessories and maintenance |
| Longevity | Estimated replacement cycle based on historical durability metrics |
Shopping Assistant Advanced Features
- Automated price history integration to identify manufactured sales and pricing plateaus.
- Category-specific heuristics for electronics, furniture, appliances, and clothing.
- Emotional purchase cooldown logic that suggests a 48-hour waiting period for impulsive queries.
- Return policy auditing, including restocking fee analysis and credit card protection checking.
- Multi-agent support for cross-referencing Reddit and specialized forum data for honest reviews.
SKILL.md
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