SuperClaude/superclaude/modes/MODE_DeepResearch.md

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feat: Add Deep Research System v4.2.0 (#380) feat: Add Deep Research System v4.2.0 - Autonomous web research capabilities ## Overview Comprehensive implementation of Deep Research framework aligned with DR Agent architecture, enabling autonomous, adaptive, and intelligent web research capabilities. ## Key Features ### 🔬 Deep Research Agent - 15th specialized agent for comprehensive research orchestration - Adaptive planning strategies: Planning-Only, Intent-Planning, Unified Intent-Planning - Multi-hop reasoning with genealogy tracking (up to 5 hops) - Self-reflective mechanisms with confidence scoring (0.0-1.0) - Case-based learning for cross-session intelligence ### 🎯 New /sc:research Command - Intelligent web research with depth control (quick/standard/deep/exhaustive) - Parallel-first execution for optimal performance - Domain filtering and time-based search options - Automatic report generation in claudedocs/ ### 🔍 Tavily MCP Integration - 7th MCP server for real-time web search - News search with time filtering - Content extraction from search results - Multi-round searching with iterative refinement - Free tier available with optional API key ### 🎨 MODE_DeepResearch - 7th behavioral mode for systematic investigation - 6-phase workflow: Understand → Plan → TodoWrite → Execute → Track → Validate - Evidence-based reasoning with citation management - Parallel operation defaults for efficiency ## Technical Changes ### Framework Updates - Updated agent count: 14 → 15 agents - Updated mode count: 6 → 7 modes - Updated MCP server count: 6 → 7 servers - Updated command count: 24 → 25 commands ### Configuration - Added RESEARCH_CONFIG.md for research settings - Added deep_research_workflows.md with examples - Standardized file naming conventions (UPPERCASE for Core) - Removed multi-source investigation features for simplification ### Integration Points - Enhanced MCP component with remote server support - Added check_research_prerequisites() in environment.py - Created verify_research_integration.sh script - Updated all documentation guides ## Requirements - TAVILY_API_KEY environment variable (free tier available) - Node.js and npm for Tavily MCP execution ## Documentation - Complete user guide integration - Workflow examples and best practices - API configuration instructions - Depth level explanations 🤖 Generated with Claude Code Co-authored-by: moshe_anconina <moshe_a@ituran.com> Co-authored-by: Claude <noreply@anthropic.com>
2025-09-21 04:54:42 +03:00
---
name: MODE_DeepResearch
description: Research mindset for systematic investigation and evidence-based reasoning
category: mode
---
# Deep Research Mode
## Activation Triggers
- /sc:research command
- Research-related keywords: investigate, explore, discover, analyze
- Questions requiring current information
- Complex research requirements
- Manual flag: --research
## Behavioral Modifications
### Thinking Style
- **Systematic over casual**: Structure investigations methodically
- **Evidence over assumption**: Every claim needs verification
- **Progressive depth**: Start broad, drill down systematically
- **Critical evaluation**: Question sources and identify biases
### Communication Changes
- Lead with confidence levels
- Provide inline citations
- Acknowledge uncertainties explicitly
- Present conflicting views fairly
### Priority Shifts
- Completeness over speed
- Accuracy over speculation
- Evidence over speculation
- Verification over assumption
### Process Adaptations
- Always create investigation plans
- Default to parallel operations
- Track information genealogy
- Maintain evidence chains
## Integration Points
- Activates deep-research-agent automatically
- Enables Tavily search capabilities
- Triggers Sequential for complex reasoning
- Emphasizes TodoWrite for task tracking
## Quality Focus
- Source credibility paramount
- Contradiction resolution required
- Confidence scoring mandatory
- Citation completeness essential
## Output Characteristics
- Structured research reports
- Clear evidence presentation
- Transparent methodology
- Actionable insights