webshop-search-formulator

name

webshop-search-formulator

description

This skill generates effective search keywords based on parsed product criteria. It is triggered after query parsing, when the agent needs to perform an initial product search on an e-commerce platform. The skill takes structured attributes (e.g., 'women's size 5 patent-beige high heel') and produces a concise, platform-appropriate search string designed to return relevant results, balancing specificity with recall.

Skill: Webshop Search Formulator

Purpose

You are an expert at formulating the initial search query for an e-commerce product search. Your goal is to translate a structured set of product requirements into a concise, effective search string that will yield relevant results on a platform like Amazon.

Action-Space Boundary

This skill is reasoning guidance, not a WebShop UI button. Do not output actions such as click[webshop-search-formulator] or activate webshop-search-formulator. Convert the selected keywords into a valid search[...] action.

Core Workflow

1. Input: You receive a parsed query containing key product attributes (e.g., category, type, size, color, material, price limit).
2. Process: Analyze the attributes to identify the most critical, distinguishing features for the initial search. Prioritize attributes that will filter the results meaningfully without being overly restrictive.
3. Output: Generate a single, well-formatted search[keywords] action string.

Key Principles for Search Formulation

• Balance Specificity & Recall: Start with a moderately specific query. Including 2-3 core attributes (e.g., size 5 patent-beige high heel) is better than a single generic term (high heel) or an overly long list of all attributes.
• Prioritize Distinctive Attributes: Favor attributes that uniquely identify the product variant (e.g., "patent-beige", "size 5") over very common ones (e.g., "women's") in the initial search.
• Use Natural Keyword Order: Place the most important or specific terms first. Mimic how a user might type the query.
• Exclude Non-Searchable Filters: Do not include filters typically applied after the search (e.g., price ranges like < $90) in the initial keyword string. These are for later refinement.
• Standardize Formatting: Use lowercase, avoid special characters, and separate keywords with spaces.

EvoSkill Recovery Policy: Staged Search and Commit Budget

For long shopping instructions, use a staged search plan instead of a single overloaded query.

1. Start with product type plus the two or three most distinctive searchable attributes. Do not include price, care instructions, or every material word in the initial search.
2. If the first page is weak, issue one revised search that preserves the product type and rare attributes while dropping noisy adjectives.
3. Keep a running best candidate across result pages. Track which hard constraints it satisfies and which soft constraints still need detail-page verification.
4. Do not spend the full budget on pagination. After two result pages, click the best candidate seen so far if it is within the displayed price budget or has a price range whose low end is within budget.
5. Only conclude "no match" after at least one plausible candidate has been inspected on its detail page or all visible candidates fail a hard constraint.

Example from Trajectory

Parsed Instruction: woman's us size 5 high heel shoe with a rubber sole and color patent-beige, and price lower than 90.00 dollars Effective Search: search[size 5 patent-beige high heel] Rationale: "size 5" and "patent-beige" are the most specific, distinguishing attributes. "high heel" defines the product type. "rubber sole" and price filter are omitted from the initial search to avoid prematurely limiting potentially valid results.

Action Format

Your final output must be a single action in the exact format: