webshop-result-filter

name: webshop-result-filter description: >- Filters search results by evaluating product listings against specific user constraints like price, features, or ratings. Use when you are on a search results page and need to systematically identify which products meet all given criteria before selecting one for closer inspection. Takes a list of products with their details and outputs a filtered subset that meets the defined requirements.

Skill: webshop-result-filter

When to Use

Activate this skill when you are on a search results page in a web shopping environment and need to systematically evaluate which products meet a user's specific, multi-faceted requirements (e.g., "price lower than 40.00 dollars" AND "natural looking").

Core Instruction

1. Parse the Instruction: Extract the user's constraints from the instruction. Common constraints include:

   • Price: A maximum or target price (e.g., price lower than 40.00 dollars).
   • Features: Specific attributes or keywords (e.g., natural looking, long, clip-in).
   • Ratings: A minimum rating threshold (if available in the observation).

2. Validate Constraints: Confirm you have at least one constraint extracted before proceeding. If the instruction contains no filterable criteria, skip filtering and select the first available product.

3. Parse the Observation: Extract the list of products from the search results page (Product ID, Title, Price, Rating if available).

4. Apply Filters: For each product, check it against all extracted constraints:

   • Price Filter: Is the product price strictly below the user's maximum?
   • Keyword/Feature Filter: Does the title/description contain the required feature keywords?
   • Rating Filter: If a rating constraint exists, does the product meet the minimum?

5. Output Decision: Select the first product that passes all criteria as the primary candidate for click[product_id]. If no product passes:

   • Try click[Next >] to check additional result pages.
   • If no more pages, use search[refined keywords] with adjusted terms.

Example from Trajectory

• User Instruction: i need a long clip-in hair extension which is natural looking, and price lower than 40.00 dollars
• Extracted Constraints:
  • price < 40.00
  • Keywords: natural (implied from "natural looking")
• Observation (First Page): Contains ~10 products with IDs, titles, and prices.
• Filtering Process:
  1. B09C337K8S: Price $29.99 (< $40.00). Title contains "Natural Looking". PASSES.
  2. B093BKWHFK: Price $63.99 (> $40.00). FAILS on price.
  3. B099K9Z9L2: Price $43.99 (> $40.00). FAILS on price.
• Result: B09C337K8S is selected as the top matching candidate.

Next Action

After identifying a matching product, click on it for more details:

Thought: [Summarize which constraints each product passed or failed, justify your selection.] Action: click[<matching_product_id>]