search the web for information when user explicitly requests web searches, by identifying the nature of the request and delegating to appropriate subagents (@code-researcher for documentation/code examples, @web-researcher for general information)
An intelligent routing skill that analyzes user web search requests and delegates to specialized subagents based on the nature of the information needed. Optimizes search quality by matching request type to the most appropriate search tool.
When to Use
Use this skill when:
User explicitly requests to search the web
User asks to "look up", "find information about", or "search for" something
Current information or documentation beyond knowledge cutoff is needed
User needs code examples, API documentation, or technical references
User requires factual information, trends, or recent developments
Available Subagents
@code-researcher
Capabilities: Equipped with exa and context7 MCP tools
Best for:
Retrieving API documentation and library references
相關技能
Finding code examples and implementation patterns
Searching GitHub repositories for solutions
Accessing StackOverflow discussions and technical Q&A
Looking up framework/library usage patterns
Finding SDK and package documentation
Strengths: High-quality, developer-focused results from curated technical sources
@web-researcher
Capabilities: Broad web search and fact-checking tools
Best for:
General information and encyclopedia-style content
Current events and news
Product research and comparisons
Company information and business intelligence
Trends, statistics, and market data
Non-technical documentation and guides
Fact-checking and verification
Strengths: Comprehensive coverage of general knowledge domains
Workflow
Step 1: Analyze the Request
Examine the user's query to determine:
Domain: Is it technical/programming-related or general knowledge?
Information type: Documentation, examples, facts, trends, explanations?
Specificity: Narrow technical query vs broad exploratory search?
Recency requirements: Does it need current/real-time information?
Step 2: Formulate Search Strategy
Based on analysis, determine:
Primary subagent: Which subagent is best suited?
Query refinement: Should the query be rephrased for better results?
Fallback plan: What to do if primary search yields poor results?
Decision Criteria:
IF query contains: APIs, libraries, frameworks, code, packages, SDKs, programming languages
→ Use @code-researcher
ELSE IF query about: documentation for dev tools, implementation examples, technical patterns
→ Use @code-researcher
ELSE IF query about: general knowledge, news, products, companies, trends, facts
→ Use @web-researcher
ELSE IF uncertain but has technical keywords
→ Start with @code-researcher, fallback to @web-researcher
ELSE
→ Use @web-researcher
Step 3: Delegate to Subagent
Invoke the selected subagent with:
Clear, specific instructions on what information to find
Context from the user's original request
Expected format or depth of results
Any constraints (date ranges, specific sources, etc.)
Delegation template:
Search for [specific information] related to [user's topic].
Focus on [documentation/examples/explanations/etc.].
Return [code examples/API references/factual summary/etc.].
Step 4: Evaluate Results
When subagent returns:
Quality check: Are results relevant and comprehensive?
Completeness: Does it answer the user's question?
Accuracy: Do results appear credible and current?
Step 5: Fallback Strategy (if needed)
If primary subagent results are insufficient:
@code-researcher → @web-researcher: When technical docs are sparse, try broader web search
@web-researcher → @code-researcher: When general search returns too much code/technical content
Combine results: Use both subagents for hybrid queries (e.g., "how does company X implement feature Y?")
Fallback scenarios:
No relevant results found
Results too technical or not technical enough
Missing crucial context or examples
User's question spans multiple domains
Instructions
Best Practices
Be specific: Provide clear search objectives to subagents
Consider recency: If topic is time-sensitive, explicitly request current information
Verify sources: Note when results come from authoritative vs community sources
Iterate if needed: Don't hesitate to refine and re-search if first attempt is poor
Combine when useful: For complex queries, use both subagents to get comprehensive coverage
Query Refinement Tips
Extract key technical terms from user's natural language query
Remove ambiguous words and clarify acronyms
Add context about programming language or framework if missing
Specify version numbers when relevant (e.g., "React 18 hooks" vs "React hooks")
Common Patterns
Pattern 1: Pure technical query
User: "How do I use async/await in Python?"
→ @code-researcher: Search for Python async/await documentation and examples
Pattern 2: General information query
User: "What are the latest trends in AI?"
→ @web-researcher: Search for recent AI trends and developments
Pattern 3: Hybrid query
User: "How does Stripe handle payment processing?"
→ @code-researcher: Get Stripe API documentation and integration examples
→ @web-researcher: Get Stripe's business model and payment flow overview
→ Combine both for comprehensive answer
Pattern 4: Fallback scenario
User: "How to implement OAuth in my app?"
→ Try @code-researcher first for technical implementation
→ If results too abstract, fallback to @web-researcher for conceptual guides
Report Format
After subagent(s) complete their search, structure the response as:
Summary
Brief overview of what was found
Which subagent(s) were used and why
Key Findings
Main points from the search results
Relevant code examples or documentation links
Important caveats or considerations
Sources
Note primary sources consulted
Indicate if results are from official docs, community resources, or general web
Recommendations
Suggest next steps if applicable
Point out gaps that might need deeper research
Mention related topics worth exploring
Keep the report concise and focused on addressing the user's original request. Avoid verbose explanations of the search process unless the user specifically wants to understand the methodology.