Yula Web Search

Problem Addressed

Large model workflows often need current facts such as news, prices, availability, or page text. Relying on the model's prior knowledge can become stale, while commercial search APIs may add keys, quotas, and cost. yula-web-search is designed for a lightweight local loop: search, extract the top pages, and summarize the answer without extra configuration.

How It Works

The skill runs an ordered retrieval pipeline:

• Search cn.bing.com first: sends curl requests and parses HTML to collect the top 5–8 result titles and URLs.
• Filter relevance: ranks titles by query keyword overlap, prioritizing official or major portal sites, and keeps only 2–3 URLs.
• Extract page text: fetches each selected URL, converts HTML to text, and truncates around 2000 characters per page to control token size.
• Synthesize the answer: merges the extracted content into a coherent response covering prices, specs, status, or other key facts.
• Fall back gracefully: if Bing fails, it tries google.com; requests use a ~20 second timeout and a browser-like User-Agent.

Boundaries

Use it for up-to-date web information, Chinese-language queries, product status, and page-text extraction. Avoid it for weather forecasts, local file search, or questions already answerable from context. Keep request volume modest, respect non-commercial use, and expect a fallback to general knowledge if all search paths fail.