Multi Search Engine

The Problem It Solves

When conducting technical research or market analysis, relying on a single search engine often presents significant limitations. Different engines have varying index scopes, ranking algorithms, and presentation styles, leading to three core issues: incomplete information coverage where critical sources may be missed; high duplication of results requiring manual cleanup; and a lack of a unified, cross-platform view for comparison. For instance, searching for an open-source library might yield Chinese community discussions on Baidu, international resources on Google, and unique academic references on Bing. Manually switching between tabs is not only time-consuming but also makes systematic integration of information difficult.

How the Skill Works

This skill, @zcwl/multi-search-engine, functions as a tool within an AI Agent to automate this process. Its core mechanics revolve around multi-engine aggregation and intelligent processing.

• Core Capabilities:
• Engine Integration: It natively supports 16 search engines—7 domestic (e.g., Baidu, Sogou) and 9 global (e.g., Google, Bing, DuckDuckGo). The skill interacts with these engines via standardized API interfaces.
• Result Aggregation & Deduplication: After executing a query, it calls the specified engines (e.g., google,baidu). It then performs intelligent aggregation on the raw results. Using URL or content fingerprints, it deduplicates entries and applies an initial relevance-based sorting to output a consolidated list.

• Structured Output: The output is not a simple collection of links but structured data containing fields like title, snippet, and source engine. This facilitates further analysis or formatting by the Agent.

• Key Workflow:
  1. Input Parameters: The user provides a required query (search keyword) and an optional engines (list of engines to use).
  2. Search Execution: The skill sequentially or concurrently calls the search APIs of the specified engines in the background.
  3. Post-Processing: The raw results undergo deduplication, cleaning, and sorting.
  4. Result Delivery: The processed, aggregated results are returned to the Agent in a standardized format.

The entire process is orchestrated using the Agent's built-in LLM API, requiring no complex web scraping or parsing scripts from the user.

Applicable Boundaries & Caveats

This skill is focused on retrieving and integrating publicly accessible web information and is not a universal solution. Its boundaries should be clear:

• Not Suitable For: Private data search that requires real-time indexing and full-text search (e.g., internal enterprise document libraries). It cannot access data sources behind specific authentication or firewalls.
• Result Limitations: The quality of aggregated results depends on the raw data returned by each engine. For extremely niche or time-sensitive information, combining it with domain-specific engines may still be necessary.
• Usage Notes:
• For complex queries, consider starting with a broad query to get an overview, then use the engines parameter to focus on specific engines for deeper filtering.
• The semantic search and intelligent summary capabilities available in the paid version can further enhance efficiency but require appropriate authorization.
• When handling multilingual queries, the skill will mix calls to Chinese and English engines, potentially returning results in multiple languages. Language filtering may need to be handled by the Agent or user.