webshop-search-formulator
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npx mdskill add zjunlp/SkillNet/webshop-search-formulatorCraft precise search strings from parsed product criteria.
- Converts structured attributes into concise e-commerce search queries.
- Depends on parsed query data containing category, size, color, and price.
- Prioritizes distinguishing features to balance specificity and recall.
- Outputs a single formatted search action string for execution.
SKILL.md
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--- 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. ## 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. ## 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:
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