webshop-result-analyzer
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npx mdskill add zjunlp/SkillNet/webshop-result-analyzerFilter search results to prioritize products matching specific criteria.
- Identifies items fitting user-defined attributes like size, color, and price.
- Depends on product listings formatted with IDs, titles, and price ranges.
- Scores candidates by matching textual attributes against required specifications.
- Outputs a ranked list of product IDs or links for further inspection.
SKILL.md
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--- name: webshop-result-analyzer description: This skill evaluates a list of search results against the target product criteria. Trigger it when a search result page is observed, to identify promising product listings. It analyzes each result's title, price, and brief description to shortlist items that best match the required attributes (like size, color, and price), outputting a prioritized list of candidate product IDs or links for further inspection. --- # Instructions Trigger this skill when you observe a search result page (e.g., containing "Page 1 (Total results: 50)" and multiple product listings). ## 1. Extract User Requirements First, parse the user's instruction from the observation. Identify the following key attributes: - **Product Type:** (e.g., "woman's us size 5 high heel shoe") - **Specific Attributes:** (e.g., "rubber sole", "color patent-beige") - **Price Constraint:** (e.g., "price lower than 90.00 dollars") ## 2. Analyze Search Results For each product listing in the observation (typically formatted as `[ASIN/Product ID] [SEP] [Title] [SEP] [Price Range]`): 1. Extract the **Product ID** (e.g., B09GXNYJCD). 2. Extract the **Product Title**. 3. Extract the **Price**. Convert any range (e.g., "$49.99 to $54.99") to its maximum value for comparison against the budget. 4. Perform a **textual match** between the title/description and the required attributes (size, color, material like "rubber", product type). ## 3. Score and Prioritize Use the bundled Python script `analyze_results.py` to perform a consistent, deterministic analysis. 1. **Run the script** with the extracted user requirements and the list of product data. 2. The script will output a **prioritized list** of candidate Product IDs, sorted by a match score. ## 4. Output and Next Action Present the analysis in this format:
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