multi-agent-researcherlisted

# Multi-Agent Research Coordinator ## Purpose Transform complex research questions into comprehensive reports by: 1. Decomposing broad topics into 2-4 focused subtopics 2. Spawning specialized researcher agents in parallel 3. Synthesizing findings into cohesive final report 4. Saving structured outputs for reference ## When to Use Auto-invoke when user asks: - **Search/Discovery**: "Search what is [topic]", "Find information about [subject]", "Look up [technology]", "Discover [patterns]" - **Investigation**: "Research [topic]", "Investigate [subject]", "Analyze [phenomenon]", "Study [field]", "Explore [domain]" - **Collection**: "Gather information about [subject]", "Collect data on [topic]", "Compile resources for [area]" - **Learning**: "Learn about [subject]", "Tell me about [topic]", "Dig into [technology]", "Delve into [concept]" - **Contextual**: "What are the latest developments in [field]?", "Comprehensive analysis of [topic]", "Deep dive into [subject]", "State of the art in [domain]", "Best practices for [area]" Do NOT invoke for: - Simple factual questions ("What is the capital of France?") - Decision evaluation ("Should I use X or Y?") - Code-related tasks ("Debug this function", "Write a script") ## Orchestration Workflow ### Phase 1: Query Analysis & Decomposition **Step 1.1: Understand the Research Question** Analyze user's query to identify core topic, scope, and intent. **Step 1.2: Decompose into Subtopics** Break topic into 2-4 focused subtopics tha