Deep Research Engine

The Problem It Solves

Engineers and researchers often face fragmented information sources when tackling complex queries. Manually checking local files, running multiple web searches, and cross-verifying results is time-consuming and risks missing key insights. Traditional search tools typically return disjointed snippets without deep analysis or structured synthesis, leading to incomplete or superficial answers.

How the Skill Works

The deep-research skill automates this workflow. It starts with a local knowledge base search, scanning predefined files (e.g., 02_projects/knowledge-base/产品知识.md) to extract relevant content. Next, it triggers multi-platform internet searches, using web_search for general queries and web_fetch for detailed page retrieval; if the 07技能 skill is configured, it extends to platforms like Twitter, YouTube, and Xiaohongshu for broader coverage.

Key steps include:
- Keyword optimization: Dynamically refining search terms based on local results to improve relevance.
- Cross-validation: Comparing information from different sources to assess credibility and timeliness.
- Integrated analysis: Combining local knowledge, web data, and inherent insights into categorized, deduplicated, and deep analysis.
- Comprehensive output: Generating structured responses (background → analysis → conclusions → recommendations) with data sources and references.

The skill embeds a problem-priority resolution mechanism. On encountering errors or insufficient information, it follows a check config → check dependencies → check permissions → try alternatives path, attempting at least three solutions and logging issues to the knowledge base for continuous improvement.

Use Cases and Limitations

This skill is ideal for deep research scenarios requiring multi-source synthesis, such as product inquiries, industry trend analysis, or competitor comparisons. However, it depends on tool configurations (like web_search and the optional 07技能) and may be limited in offline or fault states. Search results are subject to API rate limits, so timeliness must be prioritized—use the latest data when possible. For privacy, the skill avoids leaking sensitive information during web fetching. While outputs aim for depth, users should still validate with professional judgment, especially in highly dynamic or confidential domains.