A sophisticated AI-driven tool that aggregates e-commerce prices, assesses purchasing risks, and provides structured buying recommendations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install price-comparison-analyzer
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 price-comparison-analyzer 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 Price Comparison Analyzer?
The Price Comparison Analyzer is a professional-grade tool within the Openclaw Skills ecosystem designed to move beyond simple price scraping. It provides a multi-dimensional analysis of product offerings across major e-commerce platforms, ensuring users balance price with purchasing risk. By leveraging statistical modeling, it evaluates price distributions to identify anomalies, protecting users from deceptive pricing and conditions.
Built to facilitate rapid decision-making, this skill synthesizes complex market data into tiered insights. It doesn't just find the lowest number; it analyzes seller reputation, after-sales reliability, and the complexity of discount conditions to offer a clear path to the best overall value.
Price Comparison Analyzer Use Cases
- Determining if a specific product price is within the normal market range.
- Comparing detailed SKUs across major retailers like JD.com, Tmall, and Pinduoduo.
- Identifying high-risk offers that appear too good to be true based on statistical deviations.
- Selecting the best purchase channel for high-value electronics based on after-sales security.
- Evaluating the actual final cost after accounting for platform coupons and membership requirements.
How Price Comparison Analyzer Works
- Semantic Alignment: The agent cleans product titles and normalizes SKUs to ensure comparison between identical items, filtering out marketing noise.
- Price Collection: It aggregates current pricing data, including post-coupon totals, limited-time offers, and membership-specific discounts.
- Data Validation: The system runs validation scripts to ensure all collected data points meet technical specifications and format requirements.
- Statistical Modeling: Using numpy and scipy, the tool establishes price distributions (P10-P90) to define market norms.
- Risk Assessment: A rule-based engine calculates risk levels based on channel reliability, condition complexity, and price deviation.
- Multi-Layer Output: Generates structured JSON for user decisions (L1), natural language explanations (L2), and deep statistical evidence for internal agent use (L3).
Price Comparison Analyzer Setup
To integrate this tool into your Openclaw Skills workflow, ensure your environment meets the following requirements:
# Install necessary statistical dependencies
pip install numpy==1.24.0 scipy==1.10.0
Configure your environment to reference the internal scripts scripts/price_analyzer.py and scripts/data_validator.py for core logic execution.
Price Comparison Analyzer Data Schema & Taxonomy
The skill organizes its analysis into a structured hierarchy to serve different user needs:
| Data Layer | Target | Key Metadata Tags |
|---|---|---|
| L1: Decision | End User | platform, price, risk_level, tag, reason |
| L2: Explanation | Follow-up | natural_language_rationale, risk_context |
| L3: Evidence | Internal Agent | price_percentiles (P10-P90), channel_risk_scores, deviation_metrics |
All recommendations are categorized with tags such as Best Value, Most Reliable, or Lowest Price.
Price Comparison Analyzer Advanced Features
- SKU Normalization: Intelligently distinguishes between base products, bundles, and marketing gift sets to prevent false comparisons.
- Quantitative Anomaly Detection: Automatically flags prices falling below P10 x 0.9 as high-risk anomalies.
- Rule-Based Risk Scoring: Evaluates the complexity of discounts, requiring the agent to identify if a price depends on new-user status or multi-item bundles.
- Tiered Logic Architecture: Separates front-end decision-making from back-end statistical modeling to maintain a clean user experience in Openclaw Skills.
SKILL.md
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