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>
2025-12-18 02:06:36 +00:00 · 2025-09-21 04:54:42 +03:00 · 2025-09-21 04:54:42 +03:00 · f7cb0f7eb7
commit f7cb0f7eb7
parent e4f2f82aa9
22 changed files with 2169 additions and 39 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -7,6 +7,37 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 ## [4.2.0] - 2025-09-18
 ### Added
 - **Deep Research System** - Complete implementation of autonomous web research capabilities
  - New `/sc:research` command for intelligent web research with DR Agent architecture
  - `deep-research-agent` - 15th specialized agent for research orchestration
  - `MODE_DeepResearch` - 7th behavioral mode for research workflows
  - Tavily MCP integration (7th MCP server) for real-time web search
  - Research configuration system (`RESEARCH_CONFIG.md`)
  - Comprehensive workflow examples (`deep_research_workflows.md`)
  - Three planning strategies: Planning-Only, Intent-to-Planning, Unified Intent-Planning
  - Multi-hop reasoning with genealogy tracking for complex queries
  - Case-based reasoning for learning from past research patterns
 ### Changed
 - Updated agent count from 14 to 15 (added deep-research-agent)
 - Updated mode count from 6 to 7 (added MODE_DeepResearch)
 - Updated MCP server count from 6 to 7 (added Tavily)
 - Updated command count from 24 to 25 (added /sc:research)
 - Enhanced MCP component with remote server support for Tavily
 - Added `_install_remote_mcp_server` method to handle remote MCP configurations
 ### Technical
 - Added Tavily to `server_docs_map` in `setup/components/mcp_docs.py`
 - Implemented remote MCP server handler in `setup/components/mcp.py`
 - Added `check_research_prerequisites()` function in `setup/utils/environment.py`
 - Created verification script `scripts/verify_research_integration.sh`
 ### Requirements
 - `TAVILY_API_KEY` environment variable for web search functionality
 - Node.js and npm for Tavily MCP execution
 ## [4.1.4] - 2025-09-20
 ### Added
 - Comprehensive flag documentation integrated into `/sc:help` command
@ -19,7 +50,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Smart server merging (existing + selected + previously installed)
 - Documentation cleanup: removed non-existent commands (sc:fix, sc:simple-pix, sc:update, sc:develop, sc:modernize, sc:simple-fix)
 - CLI logic to allow mcp_docs installation without server selection
 ### Changed
 - MCP component now supports true incremental installation
 - mcp_docs component auto-detects and installs documentation for all detected servers
--- a/Docs/Developer-Guide/technical-architecture.md
+++ b/Docs/Developer-Guide/technical-architecture.md
@ -34,14 +34,19 @@ This guide documents how SuperClaude's Context-Oriented Configuration Framework
 ├── MCP_Playwright.md               # Playwright MCP integration
 ├── MCP_Sequential.md               # Sequential MCP integration
 ├── MCP_Serena.md                   # Serena MCP integration
 ├── MCP_Tavily.md                   # Tavily MCP integration
 ├── MCP_Zig.md                      # Zig MCP integration
 ├── MODE_Brainstorming.md           # Collaborative discovery mode
 ├── MODE_Business_Panel.md          # Business expert panel mode
 ├── MODE_DeepResearch.md            # Deep research mode
 ├── MODE_Introspection.md           # Transparent reasoning mode
 ├── MODE_Orchestration.md           # Tool coordination mode
 ├── MODE_Task_Management.md         # Task orchestration mode
 ├── MODE_Token_Efficiency.md        # Compressed communication mode
-├── agents/                         # Domain specialist contexts (14 total)
+├── agents/                         # Domain specialist contexts (19 total)
 │   ├── backend-architect.md        # Backend expertise
 │   ├── business-panel-experts.md   # Business strategy panel
 │   ├── deep-research-agent.md      # Deep research expertise
 │   ├── devops-architect.md         # DevOps expertise
 │   ├── frontend-architect.md       # Frontend expertise
 │   ├── learning-guide.md           # Educational expertise
@ -53,27 +58,34 @@ This guide documents how SuperClaude's Context-Oriented Configuration Framework
 │   ├── root-cause-analyst.md       # Problem diagnosis expertise
 │   ├── security-engineer.md        # Security expertise
 │   ├── socratic-mentor.md          # Educational expertise
 │   ├── spec-panel-experts.md       # Specification review panel
 │   ├── system-architect.md         # System design expertise
-│   └── technical-writer.md         # Documentation expertise
+│   ├── technical-writer.md         # Documentation expertise
 │   ├── test-runner.md              # Test execution expertise
 │   └── wave-orchestrator.md        # Wave orchestration patterns
 └── commands/                       # Workflow pattern contexts
-    └── sc/                         # SuperClaude command namespace (21 total)
+    └── sc/                         # SuperClaude command namespace (25 total)
        ├── analyze.md              # Analysis patterns
        ├── brainstorm.md           # Discovery patterns
        ├── build.md                # Build patterns
        ├── business-panel.md       # Business expert panel patterns
        ├── cleanup.md              # Cleanup patterns
        ├── design.md               # Design patterns
        ├── document.md             # Documentation patterns
        ├── estimate.md             # Estimation patterns
        ├── explain.md              # Explanation patterns
        ├── git.md                  # Git workflow patterns
        ├── help.md                 # Help and command listing
        ├── implement.md            # Implementation patterns
        ├── improve.md              # Improvement patterns
        ├── index.md                # Index patterns
        ├── load.md                 # Context loading patterns
        ├── reflect.md              # Reflection patterns
        ├── research.md             # Deep research patterns
        ├── save.md                 # Session persistence patterns
        ├── select-tool.md          # Tool selection patterns
        ├── spawn.md                # Multi-agent patterns
        ├── spec-panel.md           # Specification review panel
        ├── task.md                 # Task management patterns
        ├── test.md                 # Testing patterns
        ├── troubleshoot.md         # Troubleshooting patterns
@ -112,9 +124,12 @@ The main `CLAUDE.md` file uses an import system to load multiple context files:
@MCP_Playwright.md         # Playwright MCP integration
@MCP_Sequential.md         # Sequential MCP integration
@MCP_Serena.md             # Serena MCP integration
@MCP_Tavily.md             # Tavily MCP integration
@MCP_Zig.md                # Zig MCP integration
 *CRITICAL*
@MODE_Brainstorming.md     # Collaborative discovery mode
@MODE_Business_Panel.md    # Business expert panel mode
@MODE_DeepResearch.md      # Deep research mode
@MODE_Introspection.md     # Transparent reasoning mode
@MODE_Task_Management.md   # Task orchestration mode
@MODE_Orchestration.md     # Tool coordination mode
--- a/Docs/User-Guide/agents.md
+++ b/Docs/User-Guide/agents.md
@ -1,6 +1,6 @@
 # SuperClaude Agents Guide 🤖
-SuperClaude provides 14 domain specialist agents that Claude Code can invoke for specialized expertise.
+SuperClaude provides 15 domain specialist agents that Claude Code can invoke for specialized expertise.
 ## 🧪 Testing Agent Activation
@ -243,6 +243,48 @@ Task Analysis →
 **Works Best With**: system-architect (infrastructure planning), security-engineer (compliance), performance-engineer (monitoring)
 ---
 ### deep-research-agent 🔬
 **Expertise**: Comprehensive research with adaptive strategies and multi-hop reasoning
 **Auto-Activation**:
 - Keywords: "research", "investigate", "discover", "explore", "find out", "search for", "latest", "current"
 - Commands: `/sc:research` automatically activates this agent
 - Context: Complex queries requiring thorough research, current information needs, fact-checking
 - Complexity: Questions spanning multiple domains or requiring iterative exploration
 **Capabilities**:
 - **Adaptive Planning Strategies**: Planning (direct), Intent (clarify first), Unified (collaborative)
 - **Multi-Hop Reasoning**: Up to 5 levels - entity expansion, temporal progression, conceptual deepening, causal chains
 - **Self-Reflective Mechanisms**: Progress assessment after each major step with replanning triggers
 - **Evidence Management**: Clear citations, relevance scoring, uncertainty acknowledgment
 - **Tool Orchestration**: Parallel-first execution with Tavily (search), Playwright (JavaScript content), Sequential (reasoning)
 - **Learning Integration**: Pattern recognition and strategy reuse via Serena memory
 **Research Depth Levels**:
 - **Quick**: Basic search, 1 hop, summary output
 - **Standard**: Extended search, 2-3 hops, structured report (default)
 - **Deep**: Comprehensive search, 3-4 hops, detailed analysis
 - **Exhaustive**: Maximum depth, 5 hops, complete investigation
 **Examples**:
 1. **Technical Research**: `/sc:research "latest React Server Components patterns"` → Comprehensive technical research with implementation examples
 2. **Market Analysis**: `/sc:research "AI coding assistants landscape 2024" --strategy unified` → Collaborative analysis with user input
 3. **Academic Investigation**: `/sc:research "quantum computing breakthroughs" --depth exhaustive` → Comprehensive literature review with evidence chains
 **Workflow Pattern** (6-Phase):
 1. **Understand** (5-10%): Assess query complexity
 2. **Plan** (10-15%): Select strategy and identify parallel opportunities  
 3. **TodoWrite** (5%): Create adaptive task hierarchy (3-15 tasks)
 4. **Execute** (50-60%): Parallel searches and extractions
 5. **Track** (Continuous): Monitor progress and confidence
 6. **Validate** (10-15%): Verify evidence chains
 **Output**: Reports saved to `claudedocs/research_[topic]_[timestamp].md`
 **Works Best With**: system-architect (technical research), learning-guide (educational research), requirements-analyst (market research)
 ### Quality & Analysis Agents 🔍
 ### security-engineer 🔒
@ -618,6 +660,7 @@ After applying agent fixes, test with:
 | **Documentation** | "documentation", "readme", "API docs" | technical-writer |
 | **Learning** | "explain", "tutorial", "beginner", "teaching" | learning-guide |
 | **Requirements** | "requirements", "PRD", "specification" | requirements-analyst |
 | **Research** | "research", "investigate", "latest", "current" | deep-research-agent |
 ### Command-Agent Mapping
@ -631,6 +674,7 @@ After applying agent fixes, test with:
 | `/sc:design` | system-architect | Domain architects, requirements-analyst |
 | `/sc:test` | quality-engineer | security-engineer, performance-engineer |
 | `/sc:explain` | learning-guide | technical-writer, domain specialists |
 | `/sc:research` | deep-research-agent | Technical specialists, learning-guide |
 ### Effective Agent Combinations
--- a/Docs/User-Guide/commands.md
+++ b/Docs/User-Guide/commands.md
@ -1,6 +1,6 @@
 # SuperClaude Commands Guide
-SuperClaude provides 24 commands for Claude Code: `/sc:*` commands for workflows and `@agent-*` for specialists.
+SuperClaude provides 25 commands for Claude Code: `/sc:*` commands for workflows and `@agent-*` for specialists.
 ## Command Types
@ -106,7 +106,7 @@ python3 -m SuperClaude install --list-components | grep mcp
 - [Essential Commands](#essential-commands) - Start here (8 core commands)
 - [Common Workflows](#common-workflows) - Command combinations that work
- [Full Command Reference](#full-command-reference) - All 23 commands organized by category
+- [Full Command Reference](#full-command-reference) - All 25 commands organized by category
 - [Troubleshooting](#troubleshooting) - Common issues and solutions
 - [Command Index](#command-index) - Find commands by category
@ -133,6 +133,24 @@ python3 -m SuperClaude install --list-components | grep mcp
 - Discovering available commands: `/sc:help`
 - Getting a quick reminder of command names: `/sc:help`
 ### `/sc:research` - Deep Research Command
 **Purpose**: Comprehensive web research with adaptive planning and intelligent search  
 **Syntax**: `/sc:research "[query]"` `[--depth quick|standard|deep|exhaustive] [--strategy planning|intent|unified]`
 **Use Cases**:
 - Technical research: `/sc:research "latest React 19 features" --depth deep`
 - Market analysis: `/sc:research "AI coding assistant landscape 2024" --strategy unified`
 - Academic investigation: `/sc:research "quantum computing breakthroughs" --depth exhaustive`
 - Current events: `/sc:research "latest AI developments 2024"`
 **Key Capabilities**:
 - **6-Phase Workflow**: Understand → Plan → TodoWrite → Execute → Track → Validate
 - **Adaptive Depth**: Quick (basic search), Standard (extended), Deep (comprehensive), Exhaustive (maximum depth)
 - **Planning Strategies**: Planning (direct), Intent (clarify first), Unified (collaborative)
 - **Parallel Execution**: Default parallel searches and extractions
 - **Evidence Management**: Clear citations with relevance scoring
 - **Output Standards**: Reports saved to `claudedocs/research_[topic]_[timestamp].md`
 ### `/sc:implement` - Feature Development  
 **Purpose**: Full-stack feature implementation with intelligent specialist routing  
 **Syntax**: `/sc:implement "feature description"` `[--type frontend|backend|fullstack] [--focus security|performance]`  
@ -277,6 +295,7 @@ python3 -m SuperClaude install --list-components | grep mcp
 | Command | Purpose | Best For |
 |---------|---------|----------|
 | **analyze** | Code assessment | Quality audits, security reviews |
 | **research** | Web research with intelligent search | Technical research, current events, market analysis |
 | **business-panel** | Strategic analysis | Business decisions, competitive assessment |
 | **spec-panel** | Specification review | Requirements validation, architecture analysis |
 | **troubleshoot** | Problem diagnosis | Bug investigation, performance issues |
--- a/Docs/User-Guide/mcp-servers.md
+++ b/Docs/User-Guide/mcp-servers.md
@ -2,7 +2,7 @@
 ## Overview
-MCP (Model Context Protocol) servers extend Claude Code's capabilities through specialized tools. SuperClaude integrates 6 MCP servers and provides Claude with instructions on when to activate them based on your tasks.
+MCP (Model Context Protocol) servers extend Claude Code's capabilities through specialized tools. SuperClaude integrates 7 MCP servers and provides Claude with instructions on when to activate them based on your tasks.
 ### 🔍 Reality Check
 - **What MCP servers are**: External Node.js processes that provide additional tools
@ -17,6 +17,7 @@ MCP (Model Context Protocol) servers extend Claude Code's capabilities through s
 - **playwright**: Browser automation and E2E testing
 - **morphllm-fast-apply**: Pattern-based code transformations
 - **serena**: Semantic code understanding and project memory
 - **tavily**: Web search and real-time information retrieval
 ## Quick Start
@ -32,6 +33,7 @@ MCP (Model Context Protocol) servers extend Claude Code's capabilities through s
 | `test`, `e2e`, `browser` | **playwright** |
 | Multi-file edits, refactoring | **morphllm-fast-apply** |
 | Large projects, sessions | **serena** |
 | `/sc:research`, `latest`, `current` | **tavily** |
 ## Server Details
@ -119,6 +121,36 @@ export MORPH_API_KEY="your_key_here"
 /sc:refactor "extract UserService" --serena
 ```
 ### tavily 🔍
 **Purpose**: Web search and real-time information retrieval for research
 **Triggers**: `/sc:research` commands, "latest" information requests, current events, fact-checking
 **Requirements**: Node.js 16+, TAVILY_API_KEY (free tier available at https://app.tavily.com)
 ```bash
 # Automatic activation
 /sc:research "latest AI developments 2024"
 # → Performs intelligent web research
 # Manual activation  
 /sc:analyze "market trends" --tavily
 # API key setup (get free key at https://app.tavily.com)
 export TAVILY_API_KEY="tvly-your_api_key_here"
 ```
 **Capabilities:**
 - **Web Search**: Comprehensive searches with ranking and filtering
 - **News Search**: Time-filtered current events and updates
 - **Content Extraction**: Full-text extraction from search results  
 - **Domain Filtering**: Include/exclude specific domains
 - **Multi-Hop Research**: Iterative searches based on findings (up to 5 hops)
 **Research Depth Control:**
 - `--depth quick`: 5-10 sources, basic synthesis
 - `--depth standard`: 10-20 sources, structured report (default)
 - `--depth deep`: 20-40 sources, comprehensive analysis
 - `--depth exhaustive`: 40+ sources, academic-level research
 ## Configuration
 **MCP Configuration File (`~/.claude.json`):**
@ -150,6 +182,11 @@ export MORPH_API_KEY="your_key_here"
    "serena": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/oraios/serena", "serena", "start-mcp-server", "--context", "ide-assistant"]
    },
    "tavily": {
      "command": "npx",
      "args": ["-y", "@tavily/mcp@latest"],
      "env": {"TAVILY_API_KEY": "${TAVILY_API_KEY}"}
    }
  }
 }
@ -211,16 +248,21 @@ export TWENTYFIRST_API_KEY="your_key_here"
 # For Morphllm server (required for bulk transformations)
 export MORPH_API_KEY="your_key_here"
 # For Tavily server (required for web search - free tier available)
 export TAVILY_API_KEY="tvly-your_key_here"
 # Add to shell profile for persistence
 echo 'export TWENTYFIRST_API_KEY="your_key"' >> ~/.bashrc
 echo 'export MORPH_API_KEY="your_key"' >> ~/.bashrc
 echo 'export TAVILY_API_KEY="your_key"' >> ~/.bashrc
 ```
 **Environment Variable Usage:**
 - ✅ `TWENTYFIRST_API_KEY` - Required for Magic MCP server functionality
 - ✅ `MORPH_API_KEY` - Required for Morphllm MCP server functionality  
 - ✅ `TAVILY_API_KEY` - Required for Tavily MCP server functionality (free tier available)
 - ❌ Other env vars in docs - Examples only, not used by framework
- 📝 Both are paid service API keys, framework works without them
+- 📝 Magic and Morphllm are paid services, Tavily has free tier, framework works without them
 ## Server Combinations
@ -238,6 +280,8 @@ echo 'export MORPH_API_KEY="your_key"' >> ~/.bashrc
 - **Web Development**: magic + context7 + playwright  
 - **Enterprise Refactoring**: serena + morphllm + sequential-thinking
 - **Complex Analysis**: sequential-thinking + context7 + serena
 - **Deep Research**: tavily + sequential-thinking + serena + playwright
 - **Current Events**: tavily + context7 + sequential-thinking
 ## Integration
@ -245,11 +289,13 @@ echo 'export MORPH_API_KEY="your_key"' >> ~/.bashrc
 - Analysis commands automatically use Sequential + Serena
 - Implementation commands use Magic + Context7
 - Testing commands use Playwright + Sequential
 - Research commands use Tavily + Sequential + Playwright
 **With Behavioral Modes:**
 - Brainstorming Mode: Sequential for discovery
 - Task Management: Serena for persistence
 - Orchestration Mode: Optimal server selection
 - Deep Research Mode: Tavily + Sequential + Playwright coordination
 **Performance Control:**
 - Automatic resource management based on system load
--- a/Docs/User-Guide/modes.md
+++ b/Docs/User-Guide/modes.md
@ -9,6 +9,7 @@ Test modes by using `/sc:` commands - they activate automatically based on task
 |------|---------|---------------|---------------|---------------|
 | **🧠 Brainstorming** | Interactive discovery | "brainstorm", "maybe", vague requests | Socratic questions, requirement elicitation | New project planning, unclear requirements |
 | **🔍 Introspection** | Meta-cognitive analysis | Error recovery, "analyze reasoning" | Transparent thinking markers (🤔, 🎯, 💡) | Debugging, learning, optimization |
 | **🔬 Deep Research** | Systematic investigation mindset | `/sc:research`, investigation keywords | 6-phase workflow, evidence-based reasoning | Technical research, current events, market analysis |
 | **📋 Task Management** | Complex coordination | >3 steps, >2 directories | Phase breakdown, memory persistence | Multi-step operations, project management |
 | **🎯 Orchestration** | Intelligent tool selection | Multi-tool ops, high resource usage | Optimal tool routing, parallel execution | Complex analysis, performance optimization |
 | **⚡ Token Efficiency** | Compressed communication | High context usage, `--uc` flag | Symbol systems, estimated 30-50% token reduction | Resource constraints, large operations |
@ -120,6 +121,60 @@ Introspective Approach:
 ---
 ### 🔬 Deep Research Mode - Systematic Investigation Mindset
 **Purpose**: Research mindset for systematic investigation and evidence-based reasoning.
 **Auto-Activation Triggers:**
 - `/sc:research` command invocation
 - Research-related keywords: investigate, explore, discover, analyze
 - Questions requiring current information beyond knowledge cutoff
 - Complex research requirements
 - Manual flag: `--research`
 **Behavioral Modifications:**
 - **Thinking Style**: Systematic over casual, evidence over assumption, progressive depth exploration
 - **Communication**: Lead with confidence levels, provide inline citations, acknowledge uncertainties
 - **Priority Shifts**: Completeness over speed, accuracy over speculation, verification over assumption
 - **Process Adaptations**: Always create investigation plans, default to parallel operations, maintain evidence chains
 **6-Phase Research Workflow:**
 - 📋 **Understand** (5-10%): Assess query complexity and requirements
 - 📝 **Plan** (10-15%): Select strategy (planning/intent/unified) and identify parallelization
 - ✅ **TodoWrite** (5%): Create adaptive task hierarchy (3-15 tasks based on complexity)
 - 🔄 **Execute** (50-60%): Parallel-first searches and smart extraction routing
 - 📊 **Track** (Continuous): Monitor progress and update confidence scores
 - ✓ **Validate** (10-15%): Verify evidence chains and ensure completeness
 **Example Experience:**
 ```
 Standard Mode: "Here are some search results about quantum computing..."
 Deep Research Mode: 
 "📊 Research Plan: Quantum computing breakthroughs
 ✓ TodoWrite: Created 8 research tasks
 🔄 Executing parallel searches across domains
 📈 Confidence: 0.82 across 15 verified sources
 📝 Report saved: claudedocs/research_quantum_[timestamp].md"
 ```
 #### Quality Standards
 - [ ] Minimum 2 sources per claim with inline citations
 - [ ] Confidence scoring (0.0-1.0) for all findings
 - [ ] Parallel execution by default for independent operations
 - [ ] Reports saved to claudedocs/ with proper structure
 - [ ] Clear methodology and evidence presentation
 **Verify:** `/sc:research "test topic"` should create TodoWrite and execute systematically  
 **Test:** All research should include confidence scores and citations  
 **Check:** Reports should be saved to claudedocs/ automatically
 **Works Best With:**
 - **→ Task Management**: Research planning with TodoWrite integration
 - **→ Orchestration**: Parallel Tavily/Playwright coordination
 - **Manual Override**: Use `--depth` and `--strategy` for fine control
 ---
 ### 📋 Task Management Mode - Complex Coordination
 **Purpose**: Hierarchical task organization with session persistence for multi-step operations.
--- a/README.md
+++ b/README.md
@ -14,7 +14,7 @@
 <a href="https://github.com/SuperClaude-Org/SuperQwen_Framework" target="_blank">
  <img src="https://img.shields.io/badge/Try-SuperQwen_Framework-orange" alt="Try SuperQwen Framework"/>
 </a>
-  <img src="https://img.shields.io/badge/version-4.1.4-blue" alt="Version">
+  <img src="https://img.shields.io/badge/version-4.2.0-blue" alt="Version">
  <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License">
  <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" alt="PRs Welcome">
 </p>
@ -61,7 +61,7 @@
 | **Commands** | **Agents** | **Modes** | **MCP Servers** |
 |:------------:|:----------:|:---------:|:---------------:|
-| **24** | **14** | **6** | **6** |
+| **25** | **15** | **7** | **7** |
 | Slash Commands | Specialized AI | Behavioral | Integrations |
 Use the new `/sc:help` command to see a full list of all available commands.
@ -204,7 +204,8 @@ pip install --break-system-packages SuperClaude
 <td width="50%">
 ### 🤖 **Smarter Agent System**
-**14 specialized agents** with domain expertise:
+**15 specialized agents** with domain expertise:
 - Deep Research agent for autonomous web research
 - Security engineer catches real vulnerabilities
 - Frontend architect understands UI patterns
 - Automatic coordination based on context
@ -216,7 +217,7 @@ pip install --break-system-packages SuperClaude
 ### 📝 **Improved Namespace**
 **`/sc:` prefix** for all commands:
 - No conflicts with custom commands
- 23 commands covering full lifecycle
+- 25 commands covering full lifecycle
 - From brainstorming to deployment
 - Clean, organized command structure
@ -226,21 +227,23 @@ pip install --break-system-packages SuperClaude
 <td width="50%">
 ### 🔧 **MCP Server Integration**
-**6 powerful servers** working together:
+**7 powerful servers** working together:
 - **Context7** → Up-to-date documentation
 - **Sequential** → Complex analysis
 - **Magic** → UI component generation
 - **Playwright** → Browser testing
 - **Morphllm** → Bulk transformations
 - **Serena** → Session persistence
 - **Tavily** → Web search for deep research
 </td>
 <td width="50%">
 ### 🎯 **Behavioral Modes**
-**6 adaptive modes** for different contexts:
+**7 adaptive modes** for different contexts:
 - **Brainstorming** → Asks right questions
 - **Business Panel** → Multi-expert strategic analysis
 - **Deep Research** → Autonomous web research
 - **Orchestration** → Efficient tool coordination
 - **Token-Efficiency** → 30-50% context savings
 - **Task Management** → Systematic organization
@ -278,6 +281,98 @@ pip install --break-system-packages SuperClaude
 <div align="center">
 ## 🔬 **Deep Research Capabilities**
 ### **Autonomous Web Research Aligned with DR Agent Architecture**
 SuperClaude v4.2 introduces comprehensive Deep Research capabilities, enabling autonomous, adaptive, and intelligent web research.
 <table>
 <tr>
 <td width="50%">
 ### 🎯 **Adaptive Planning**
 **Three intelligent strategies:**
 - **Planning-Only**: Direct execution for clear queries
 - **Intent-Planning**: Clarification for ambiguous requests
 - **Unified**: Collaborative plan refinement (default)
 </td>
 <td width="50%">
 ### 🔄 **Multi-Hop Reasoning**
 **Up to 5 iterative searches:**
 - Entity expansion (Paper → Authors → Works)
 - Concept deepening (Topic → Details → Examples)
 - Temporal progression (Current → Historical)
 - Causal chains (Effect → Cause → Prevention)
 </td>
 </tr>
 <tr>
 <td width="50%">
 ### 📊 **Quality Scoring**
 **Confidence-based validation:**
 - Source credibility assessment (0.0-1.0)
 - Coverage completeness tracking
 - Synthesis coherence evaluation
 - Minimum threshold: 0.6, Target: 0.8
 </td>
 <td width="50%">
 ### 🧠 **Case-Based Learning**
 **Cross-session intelligence:**
 - Pattern recognition and reuse
 - Strategy optimization over time
 - Successful query formulations saved
 - Performance improvement tracking
 </td>
 </tr>
 </table>
 ### **Research Command Usage**
 ```bash
 # Basic research with automatic depth
 /sc:research "latest AI developments 2024"
 # Controlled research depth
 /sc:research "quantum computing breakthroughs" --depth exhaustive
 # Specific strategy selection
 /sc:research "market analysis" --strategy planning-only
 # Domain-filtered research
 /sc:research "React patterns" --domains "reactjs.org,github.com"
 ```
 ### **Research Depth Levels**
 | Depth | Sources | Hops | Time | Best For |
 |:-----:|:-------:|:----:|:----:|----------|
 | **Quick** | 5-10 | 1 | ~2min | Quick facts, simple queries |
 | **Standard** | 10-20 | 3 | ~5min | General research (default) |
 | **Deep** | 20-40 | 4 | ~8min | Comprehensive analysis |
 | **Exhaustive** | 40+ | 5 | ~10min | Academic-level research |
 ### **Integrated Tool Orchestration**
 The Deep Research system intelligently coordinates multiple tools:
 - **Tavily MCP**: Primary web search and discovery
 - **Playwright MCP**: Complex content extraction
 - **Sequential MCP**: Multi-step reasoning and synthesis
 - **Serena MCP**: Memory and learning persistence
 - **Context7 MCP**: Technical documentation lookup
 </div>
 ---
 <div align="center">
 ## 📚 **Documentation**
 ### **Complete Guide to SuperClaude**
@ -302,19 +397,19 @@ pip install --break-system-packages SuperClaude
 <td valign="top">
 - 🎯 [**Commands Reference**](Docs/User-Guide/commands.md)  
-  *All 23 slash commands*
+  *All 25 slash commands*
 - 🤖 [**Agents Guide**](Docs/User-Guide/agents.md)  
-  *14 specialized agents*
+  *15 specialized agents*
 - 🎨 [**Behavioral Modes**](Docs/User-Guide/modes.md)  
-  *5 adaptive modes*
+  *7 adaptive modes*
 - 🚩 [**Flags Guide**](Docs/User-Guide/flags.md)  
  *Control behaviors*
 - 🔧 [**MCP Servers**](Docs/User-Guide/mcp-servers.md)  
-  *6 server integrations*
+  *7 server integrations*
 - 💼 [**Session Management**](Docs/User-Guide/session-management.md)  
  *Save & restore state*
--- a/SuperClaude/Agents/deep-research-agent.md
+++ b/SuperClaude/Agents/deep-research-agent.md
@ -0,0 +1,185 @@
 ---
 name: deep-research-agent
 description: Specialist for comprehensive research with adaptive strategies and intelligent exploration
 category: analysis
 ---
 # Deep Research Agent
 ## Triggers
 - /sc:research command activation
 - Complex investigation requirements
 - Complex information synthesis needs
 - Academic research contexts
 - Real-time information requests
 ## Behavioral Mindset
 Think like a research scientist crossed with an investigative journalist. Apply systematic methodology, follow evidence chains, question sources critically, and synthesize findings coherently. Adapt your approach based on query complexity and information availability.
 ## Core Capabilities
 ### Adaptive Planning Strategies
 **Planning-Only** (Simple/Clear Queries)
 - Direct execution without clarification
 - Single-pass investigation
 - Straightforward synthesis
 **Intent-Planning** (Ambiguous Queries)
 - Generate clarifying questions first
 - Refine scope through interaction
 - Iterative query development
 **Unified Planning** (Complex/Collaborative)
 - Present investigation plan
 - Seek user confirmation
 - Adjust based on feedback
 ### Multi-Hop Reasoning Patterns
 **Entity Expansion**
 - Person → Affiliations → Related work
 - Company → Products → Competitors
 - Concept → Applications → Implications
 **Temporal Progression**
 - Current state → Recent changes → Historical context
 - Event → Causes → Consequences → Future implications
 **Conceptual Deepening**
 - Overview → Details → Examples → Edge cases
 - Theory → Practice → Results → Limitations
 **Causal Chains**
 - Observation → Immediate cause → Root cause
 - Problem → Contributing factors → Solutions
 Maximum hop depth: 5 levels
 Track hop genealogy for coherence
 ### Self-Reflective Mechanisms
 **Progress Assessment**
 After each major step:
 - Have I addressed the core question?
 - What gaps remain?
 - Is my confidence improving?
 - Should I adjust strategy?
 **Quality Monitoring**
 - Source credibility check
 - Information consistency verification
 - Bias detection and balance
 - Completeness evaluation
 **Replanning Triggers**
 - Confidence below 60%
 - Contradictory information >30%
 - Dead ends encountered
 - Time/resource constraints
 ### Evidence Management
 **Result Evaluation**
 - Assess information relevance
 - Check for completeness
 - Identify gaps in knowledge
 - Note limitations clearly
 **Citation Requirements**
 - Provide sources when available
 - Use inline citations for clarity
 - Note when information is uncertain
 ### Tool Orchestration
 **Search Strategy**
 1. Broad initial searches (Tavily)
 2. Identify key sources
 3. Deep extraction as needed
 4. Follow interesting leads
 **Extraction Routing**
 - Static HTML → Tavily extraction
 - JavaScript content → Playwright
 - Technical docs → Context7
 - Local context → Native tools
 **Parallel Optimization**
 - Batch similar searches
 - Concurrent extractions
 - Distributed analysis
 - Never sequential without reason
 ### Learning Integration
 **Pattern Recognition**
 - Track successful query formulations
 - Note effective extraction methods
 - Identify reliable source types
 - Learn domain-specific patterns
 **Memory Usage**
 - Check for similar past research
 - Apply successful strategies
 - Store valuable findings
 - Build knowledge over time
 ## Research Workflow
 ### Discovery Phase
 - Map information landscape
 - Identify authoritative sources
 - Detect patterns and themes
 - Find knowledge boundaries
 ### Investigation Phase
 - Deep dive into specifics
 - Cross-reference information
 - Resolve contradictions
 - Extract insights
 ### Synthesis Phase
 - Build coherent narrative
 - Create evidence chains
 - Identify remaining gaps
 - Generate recommendations
 ### Reporting Phase
 - Structure for audience
 - Add proper citations
 - Include confidence levels
 - Provide clear conclusions
 ## Quality Standards
 ### Information Quality
 - Verify key claims when possible
 - Recency preference for current topics
 - Assess information reliability
 - Bias detection and mitigation
 ### Synthesis Requirements
 - Clear fact vs interpretation
 - Transparent contradiction handling
 - Explicit confidence statements
 - Traceable reasoning chains
 ### Report Structure
 - Executive summary
 - Methodology description
 - Key findings with evidence
 - Synthesis and analysis
 - Conclusions and recommendations
 - Complete source list
 ## Performance Optimization
 - Cache search results
 - Reuse successful patterns
 - Prioritize high-value sources
 - Balance depth with time
 ## Boundaries
 **Excel at**: Current events, technical research, intelligent search, evidence-based analysis
 **Limitations**: No paywall bypass, no private data access, no speculation without evidence
--- a/SuperClaude/Commands/research.md
+++ b/SuperClaude/Commands/research.md
@ -0,0 +1,103 @@
 ---
 name: research
 description: Deep web research with adaptive planning and intelligent search
 category: command
 complexity: advanced
 mcp-servers: [tavily, sequential, playwright, serena]
 personas: [deep-research-agent]
 ---
 # /sc:research - Deep Research Command
 > **Context Framework Note**: This command activates comprehensive research capabilities with adaptive planning, multi-hop reasoning, and evidence-based synthesis.
 ## Triggers
 - Research questions beyond knowledge cutoff
 - Complex research questions
 - Current events and real-time information
 - Academic or technical research requirements
 - Market analysis and competitive intelligence
 ## Context Trigger Pattern
 ```
 /sc:research "[query]" [--depth quick|standard|deep|exhaustive] [--strategy planning|intent|unified]
 ```
 ## Behavioral Flow
 ### 1. Understand (5-10% effort)
 - Assess query complexity and ambiguity
 - Identify required information types
 - Determine resource requirements
 - Define success criteria
 ### 2. Plan (10-15% effort)
 - Select planning strategy based on complexity
 - Identify parallelization opportunities
 - Generate research question decomposition
 - Create investigation milestones
 ### 3. TodoWrite (5% effort)
 - Create adaptive task hierarchy
 - Scale tasks to query complexity (3-15 tasks)
 - Establish task dependencies
 - Set progress tracking
 ### 4. Execute (50-60% effort)
 - **Parallel-first searches**: Always batch similar queries
 - **Smart extraction**: Route by content complexity
 - **Multi-hop exploration**: Follow entity and concept chains
 - **Evidence collection**: Track sources and confidence
 ### 5. Track (Continuous)
 - Monitor TodoWrite progress
 - Update confidence scores
 - Log successful patterns
 - Identify information gaps
 ### 6. Validate (10-15% effort)
 - Verify evidence chains
 - Check source credibility
 - Resolve contradictions
 - Ensure completeness
 ## Key Patterns
 ### Parallel Execution
 - Batch all independent searches
 - Run concurrent extractions
 - Only sequential for dependencies
 ### Evidence Management
 - Track search results
 - Provide clear citations when available
 - Note uncertainties explicitly
 ### Adaptive Depth
 - **Quick**: Basic search, 1 hop, summary output
 - **Standard**: Extended search, 2-3 hops, structured report
 - **Deep**: Comprehensive search, 3-4 hops, detailed analysis
 - **Exhaustive**: Maximum depth, 5 hops, complete investigation
 ## MCP Integration
 - **Tavily**: Primary search and extraction engine
 - **Sequential**: Complex reasoning and synthesis
 - **Playwright**: JavaScript-heavy content extraction
 - **Serena**: Research session persistence
 ## Output Standards
 - Save reports to `claudedocs/research_[topic]_[timestamp].md`
 - Include executive summary
 - Provide confidence levels
 - List all sources with citations
 ## Examples
 ```
 /sc:research "latest developments in quantum computing 2024"
 /sc:research "competitive analysis of AI coding assistants" --depth deep
 /sc:research "best practices for distributed systems" --strategy unified
 ```
 ## Boundaries
 **Will**: Current information, intelligent search, evidence-based analysis
 **Won't**: Make claims without sources, skip validation, access restricted content
--- a/SuperClaude/Core/RESEARCH_CONFIG.md
+++ b/SuperClaude/Core/RESEARCH_CONFIG.md
@ -0,0 +1,446 @@
 # Deep Research Configuration
 ## Default Settings
 ```yaml
 research_defaults:
  planning_strategy: unified
  max_hops: 5
  confidence_threshold: 0.7
  memory_enabled: true
  parallelization: true
  parallel_first: true  # MANDATORY DEFAULT
  sequential_override_requires_justification: true  # NEW
 parallel_execution_rules:
  DEFAULT_MODE: PARALLEL  # EMPHASIZED
  mandatory_parallel:
    - "Multiple search queries"
    - "Batch URL extractions"
    - "Independent analyses"
    - "Non-dependent hops"
    - "Result processing"
    - "Information extraction"
  sequential_only_with_justification:
    - reason: "Explicit dependency"
      example: "Hop N requires Hop N-1 results"
    - reason: "Resource constraint"
      example: "API rate limit reached"
    - reason: "User requirement"
      example: "User requests sequential for debugging"
  parallel_optimization:
    batch_sizes:
      searches: 5
      extractions: 3
      analyses: 2
    intelligent_grouping:
      by_domain: true
      by_complexity: true
      by_resource: true
 planning_strategies:
  planning_only:
    clarification: false
    user_confirmation: false
    execution: immediate
  intent_planning:
    clarification: true
    max_questions: 3
    execution: after_clarification
  unified:
    clarification: optional
    plan_presentation: true
    user_feedback: true
    execution: after_confirmation
 hop_configuration:
  max_depth: 5
  timeout_per_hop: 60s
  parallel_hops: true
  loop_detection: true
  genealogy_tracking: true
 confidence_scoring:
  relevance_weight: 0.5
  completeness_weight: 0.5
  minimum_threshold: 0.6
  target_threshold: 0.8
 self_reflection:
  frequency: after_each_hop
  triggers:
    - confidence_below_threshold
    - contradictions_detected
    - time_elapsed_percentage: 80
    - user_intervention
  actions:
    - assess_quality
    - identify_gaps
    - consider_replanning
    - adjust_strategy
 memory_management:
  case_based_reasoning: true
  pattern_learning: true
  session_persistence: true
  cross_session_learning: true
  retention_days: 30
 tool_coordination:
  discovery_primary: tavily
  extraction_smart_routing: true
  reasoning_engine: sequential
  memory_backend: serena
  parallel_tool_calls: true
 quality_gates:
  planning_gate:
    required_elements: [objectives, strategy, success_criteria]
  execution_gate:
    min_confidence: 0.6
  synthesis_gate:
    coherence_required: true
    clarity_required: true
 extraction_settings:
  scraping_strategy: selective
  screenshot_capture: contextual
  authentication_handling: ethical
  javascript_rendering: auto_detect
  timeout_per_page: 15s
 ```
 ## Performance Optimizations
 ```yaml
 optimization_strategies:
  caching:
    - Cache Tavily search results: 1 hour
    - Cache Playwright extractions: 24 hours
    - Cache Sequential analysis: 1 hour
    - Reuse case patterns: always
  parallelization:
    - Parallel searches: max 5
    - Parallel extractions: max 3
    - Parallel analysis: max 2
    - Tool call batching: true
  resource_limits:
    - Max time per research: 10 minutes
    - Max search iterations: 10
    - Max hops: 5
    - Max memory per session: 100MB
 ```
 ## Strategy Selection Rules
 ```yaml
 strategy_selection:
  planning_only:
    indicators:
      - Clear, specific query
      - Technical documentation request
      - Well-defined scope
      - No ambiguity detected
  intent_planning:
    indicators:
      - Ambiguous terms present
      - Broad topic area
      - Multiple possible interpretations
      - User expertise unknown
  unified:
    indicators:
      - Complex multi-faceted query
      - User collaboration beneficial
      - Iterative refinement expected
      - High-stakes research
 ```
 ## Source Credibility Matrix
 ```yaml
 source_credibility:
  tier_1_sources:
    score: 0.9-1.0
    types:
      - Academic journals
      - Government publications
      - Official documentation
      - Peer-reviewed papers
  tier_2_sources:
    score: 0.7-0.9
    types:
      - Established media
      - Industry reports
      - Expert blogs
      - Technical forums
  tier_3_sources:
    score: 0.5-0.7
    types:
      - Community resources
      - User documentation
      - Social media (verified)
      - Wikipedia
  tier_4_sources:
    score: 0.3-0.5
    types:
      - User forums
      - Social media (unverified)
      - Personal blogs
      - Comments sections
 ```
 ## Depth Configurations
 ```yaml
 research_depth_profiles:
  quick:
    max_sources: 10
    max_hops: 1
    iterations: 1
    time_limit: 2 minutes
    confidence_target: 0.6
    extraction: tavily_only
  standard:
    max_sources: 20
    max_hops: 3
    iterations: 2
    time_limit: 5 minutes
    confidence_target: 0.7
    extraction: selective
  deep:
    max_sources: 40
    max_hops: 4
    iterations: 3
    time_limit: 8 minutes
    confidence_target: 0.8
    extraction: comprehensive
  exhaustive:
    max_sources: 50+
    max_hops: 5
    iterations: 5
    time_limit: 10 minutes
    confidence_target: 0.9
    extraction: all_sources
 ```
 ## Multi-Hop Patterns
 ```yaml
 hop_patterns:
  entity_expansion:
    description: "Explore entities found in previous hop"
    example: "Paper → Authors → Other works → Collaborators"
    max_branches: 3
  concept_deepening:
    description: "Drill down into concepts"
    example: "Topic → Subtopics → Details → Examples"
    max_depth: 4
  temporal_progression:
    description: "Follow chronological development"
    example: "Current → Recent → Historical → Origins"
    direction: backward
  causal_chain:
    description: "Trace cause and effect"
    example: "Effect → Immediate cause → Root cause → Prevention"
    validation: required
 ```
 ## Extraction Routing Rules
 ```yaml
 extraction_routing:
  use_tavily:
    conditions:
      - Static HTML content
      - Simple article structure
      - No JavaScript requirement
      - Public access
  use_playwright:
    conditions:
      - JavaScript rendering required
      - Dynamic content present
      - Authentication needed
      - Interactive elements
      - Screenshots required
  use_context7:
    conditions:
      - Technical documentation
      - API references
      - Framework guides
      - Library documentation
  use_native:
    conditions:
      - Local file access
      - Simple explanations
      - Code generation
      - General knowledge
 ```
 ## Case-Based Learning Schema
 ```yaml
 case_schema:
  case_id:
    format: "research_[timestamp]_[topic_hash]"
  case_content:
    query: "original research question"
    strategy_used: "planning approach"
    successful_patterns:
      - query_formulations: []
      - extraction_methods: []
      - synthesis_approaches: []
    findings:
      key_discoveries: []
      source_credibility_scores: {}
      confidence_levels: {}
    lessons_learned:
      what_worked: []
      what_failed: []
      optimizations: []
    metrics:
      time_taken: seconds
      sources_processed: count
      hops_executed: count
      confidence_achieved: float
 ```
 ## Replanning Thresholds
 ```yaml
 replanning_triggers:
  confidence_based:
    critical: < 0.4
    low: < 0.6
    acceptable: 0.6-0.7
    good: > 0.7
  time_based:
    warning: 70% of limit
    critical: 90% of limit
  quality_based:
    insufficient_sources: < 3
    contradictions: > 30%
    gaps_identified: > 50%
  user_based:
    explicit_request: immediate
    implicit_dissatisfaction: assess
 ```
 ## Output Format Templates
 ```yaml
 output_formats:
  summary:
    max_length: 500 words
    sections: [key_finding, evidence, sources]
    confidence_display: simple
  report:
    sections: [executive_summary, methodology, findings, synthesis, conclusions]
    citations: inline
    confidence_display: detailed
    visuals: included
  academic:
    sections: [abstract, introduction, methodology, literature_review, findings, discussion, conclusions]
    citations: academic_format
    confidence_display: statistical
    appendices: true
 ```
 ## Error Handling
 ```yaml
 error_handling:
  tavily_errors:
    api_key_missing: "Check TAVILY_API_KEY environment variable"
    rate_limit: "Wait and retry with exponential backoff"
    no_results: "Expand search terms or try alternatives"
  playwright_errors:
    timeout: "Skip source or increase timeout"
    navigation_failed: "Mark as inaccessible, continue"
    screenshot_failed: "Continue without visual"
  quality_errors:
    low_confidence: "Trigger replanning"
    contradictions: "Seek additional sources"
    insufficient_data: "Expand search scope"
 ```
 ## Integration Points
 ```yaml
 mcp_integration:
  tavily:
    role: primary_search
    fallback: native_websearch
  playwright:
    role: complex_extraction
    fallback: tavily_extraction
  sequential:
    role: reasoning_engine
    fallback: native_reasoning
  context7:
    role: technical_docs
    fallback: tavily_search
  serena:
    role: memory_management
    fallback: session_only
 ```
 ## Monitoring Metrics
 ```yaml
 metrics_tracking:
  performance:
    - search_latency
    - extraction_time
    - synthesis_duration
    - total_research_time
  quality:
    - confidence_scores
    - source_diversity
    - coverage_completeness
    - contradiction_rate
  efficiency:
    - cache_hit_rate
    - parallel_execution_rate
    - memory_usage
    - api_cost
  learning:
    - pattern_reuse_rate
    - strategy_success_rate
    - improvement_trajectory
 ```
--- a/SuperClaude/Examples/deep_research_workflows.md
+++ b/SuperClaude/Examples/deep_research_workflows.md
@ -0,0 +1,495 @@
 # Deep Research Workflows
 ## Example 1: Planning-Only Strategy
 ### Scenario
 Clear research question: "Latest TensorFlow 3.0 features"
 ### Execution
 ```bash
 /sc:research "Latest TensorFlow 3.0 features" --strategy planning-only --depth standard
 ```
 ### Workflow
 ```yaml
 1. Planning (Immediate):
   - Decompose: Official docs, changelog, tutorials
   - No user clarification needed
 2. Execution:
   - Hop 1: Official TensorFlow documentation
   - Hop 2: Recent tutorials and examples
   - Confidence: 0.85 achieved
 3. Synthesis:
   - Features list with examples
   - Migration guide references
   - Performance comparisons
 ```
 ## Example 2: Intent-to-Planning Strategy
 ### Scenario
 Ambiguous request: "AI safety"
 ### Execution
 ```bash
 /sc:research "AI safety" --strategy intent-planning --depth deep
 ```
 ### Workflow
 ```yaml
 1. Intent Clarification:
   Questions:
   - "Are you interested in technical AI alignment, policy/governance, or current events?"
   - "What's your background level (researcher, developer, general interest)?"
   - "Any specific AI systems or risks of concern?"
 2. User Response:
   - "Technical alignment for LLMs, researcher level"
 3. Refined Planning:
   - Focus on alignment techniques
   - Academic sources priority
   - Include recent papers
 4. Multi-Hop Execution:
   - Hop 1: Recent alignment papers
   - Hop 2: Key researchers and labs
   - Hop 3: Emerging techniques
   - Hop 4: Open problems
 5. Self-Reflection:
   - Coverage: Complete ✓
   - Depth: Adequate ✓
   - Confidence: 0.82 ✓
 ```
 ## Example 3: Unified Intent-Planning with Replanning
 ### Scenario
 Complex research: "Build AI startup competitive analysis"
 ### Execution
 ```bash
 /sc:research "Build AI startup competitive analysis" --strategy unified --hops 5
 ```
 ### Workflow
 ```yaml
 1. Initial Plan Presentation:
   Proposed Research Areas:
   - Current AI startup landscape
   - Funding and valuations
   - Technology differentiators
   - Market positioning
   - Growth strategies
   "Does this cover your needs? Any specific competitors or aspects to focus on?"
 2. User Adjustment:
   "Focus on code generation tools, include pricing and technical capabilities"
 3. Revised Multi-Hop Research:
   - Hop 1: List of code generation startups
   - Hop 2: Technical capabilities comparison
   - Hop 3: Pricing and business models
   - Hop 4: Customer reviews and adoption
   - Hop 5: Investment and growth metrics
 4. Mid-Research Replanning:
   - Low confidence on technical details (0.55)
   - Switch to Playwright for interactive demos
   - Add GitHub repository analysis
 5. Quality Gate Check:
   - Technical coverage: Improved to 0.78 ✓
   - Pricing data: Complete 0.90 ✓
   - Competitive matrix: Generated ✓
 ```
 ## Example 4: Case-Based Research with Learning
 ### Scenario
 Similar to previous research: "Rust async runtime comparison"
 ### Execution
 ```bash
 /sc:research "Rust async runtime comparison" --memory enabled
 ```
 ### Workflow
 ```yaml
 1. Case Retrieval:
   Found Similar Case:
   - "Go concurrency patterns" research
   - Successful pattern: Technical benchmarks + code examples + community feedback
 2. Adapted Strategy:
   - Use similar structure for Rust
   - Focus on: Tokio, async-std, smol
   - Include benchmarks and examples
 3. Execution with Known Patterns:
   - Skip broad searches
   - Direct to technical sources
   - Use proven extraction methods
 4. New Learning Captured:
   - Rust community prefers different metrics than Go
   - Crates.io provides useful statistics
   - Discord communities have valuable discussions
 5. Memory Update:
   - Store successful Rust research patterns
   - Note language-specific source preferences
   - Save for future Rust queries
 ```
 ## Example 5: Self-Reflective Refinement Loop
 ### Scenario
 Evolving research: "Quantum computing for optimization"
 ### Execution
 ```bash
 /sc:research "Quantum computing for optimization" --confidence 0.8 --depth exhaustive
 ```
 ### Workflow
 ```yaml
 1. Initial Research Phase:
   - Academic papers collected
   - Basic concepts understood
   - Confidence: 0.65 (below threshold)
 2. Self-Reflection Analysis:
   Gaps Identified:
   - Practical implementations missing
   - No industry use cases
   - Mathematical details unclear
 3. Replanning Decision:
   - Add industry reports
   - Include video tutorials for math
   - Search for code implementations
 4. Enhanced Research:
   - Hop 1→2: Papers → Authors → Implementations
   - Hop 3→4: Companies → Case studies
   - Hop 5: Tutorial videos for complex math
 5. Quality Achievement:
   - Confidence raised to 0.82 ✓
   - Comprehensive coverage achieved
   - Multiple perspectives included
 ```
 ## Example 6: Technical Documentation Research with Playwright
 ### Scenario
 Research the latest Next.js 14 App Router features
 ### Execution
 ```bash
 /sc:research "Next.js 14 App Router complete guide" --depth deep --scrape selective --screenshots
 ```
 ### Workflow
 ```yaml
 1. Tavily Search:
   - Find official docs, tutorials, blog posts
   - Identify JavaScript-heavy documentation sites
 2. URL Analysis:
   - Next.js docs → JavaScript rendering required
   - Blog posts → Static content, Tavily sufficient
   - Video tutorials → Need transcript extraction
 3. Playwright Navigation:
   - Navigate to official documentation
   - Handle interactive code examples
   - Capture screenshots of UI components
 4. Dynamic Extraction:
   - Extract code samples
   - Capture interactive demos
   - Document routing patterns
 5. Synthesis:
   - Combine official docs with community tutorials
   - Create comprehensive guide with visuals
   - Include code examples and best practices
 ```
 ## Example 7: Competitive Intelligence with Visual Documentation
 ### Scenario
 Analyze competitor pricing and features
 ### Execution
 ```bash
 /sc:research "AI writing assistant tools pricing features 2024" --scrape all --screenshots --interactive
 ```
 ### Workflow
 ```yaml
 1. Market Discovery:
   - Tavily finds: Jasper, Copy.ai, Writesonic, etc.
   - Identify pricing pages and feature lists
 2. Complexity Assessment:
   - Dynamic pricing calculators detected
   - Interactive feature comparisons found
   - Login-gated content identified
 3. Playwright Extraction:
   - Navigate to each pricing page
   - Interact with pricing sliders
   - Capture screenshots of pricing tiers
 4. Feature Analysis:
   - Extract feature matrices
   - Compare capabilities
   - Document limitations
 5. Report Generation:
   - Competitive positioning matrix
   - Visual pricing comparison
   - Feature gap analysis
   - Strategic recommendations
 ```
 ## Example 8: Academic Research with Authentication
 ### Scenario
 Research latest machine learning papers
 ### Execution
 ```bash
 /sc:research "transformer architecture improvements 2024" --depth exhaustive --auth --scrape auto
 ```
 ### Workflow
 ```yaml
 1. Academic Search:
   - Tavily finds papers on arXiv, IEEE, ACM
   - Identify open vs. gated content
 2. Access Strategy:
   - arXiv: Direct access, no auth needed
   - IEEE: Institutional access required
   - ACM: Mixed access levels
 3. Extraction Approach:
   - Public papers: Tavily extraction
   - Gated content: Playwright with auth
   - PDFs: Download and process
 4. Citation Network:
   - Follow reference chains
   - Identify key contributors
   - Map research lineage
 5. Literature Synthesis:
   - Chronological development
   - Key innovations identified
   - Future directions mapped
   - Comprehensive bibliography
 ```
 ## Example 9: Real-time Market Data Research
 ### Scenario
 Gather current cryptocurrency market analysis
 ### Execution
 ```bash
 /sc:research "cryptocurrency market analysis BTC ETH 2024" --scrape all --interactive --screenshots
 ```
 ### Workflow
 ```yaml
 1. Market Discovery:
   - Find: CoinMarketCap, CoinGecko, TradingView
   - Identify real-time data sources
 2. Dynamic Content Handling:
   - Playwright loads live charts
   - Capture price movements
   - Extract volume data
 3. Interactive Analysis:
   - Interact with chart timeframes
   - Toggle technical indicators
   - Capture different views
 4. Data Synthesis:
   - Current market conditions
   - Technical analysis
   - Sentiment indicators
   - Visual documentation
 5. Report Output:
   - Market snapshot with charts
   - Technical analysis summary
   - Trading volume trends
   - Risk assessment
 ```
 ## Example 10: Multi-Domain Research with Parallel Execution
 ### Scenario
 Comprehensive analysis of "AI in healthcare 2024"
 ### Execution
 ```bash
 /sc:research "AI in healthcare applications 2024" --depth exhaustive --hops 5 --parallel
 ```
 ### Workflow
 ```yaml
 1. Domain Decomposition:
   Parallel Searches:
   - Medical AI applications
   - Regulatory landscape
   - Market analysis
   - Technical implementations
   - Ethical considerations
 2. Multi-Hop Exploration:
   Each Domain:
   - Hop 1: Broad landscape
   - Hop 2: Key players
   - Hop 3: Case studies
   - Hop 4: Challenges
   - Hop 5: Future trends
 3. Cross-Domain Synthesis:
   - Medical ↔ Technical connections
   - Regulatory ↔ Market impacts
   - Ethical ↔ Implementation constraints
 4. Quality Assessment:
   - Coverage: All domains addressed
   - Depth: Sufficient detail per domain
   - Integration: Cross-domain insights
   - Confidence: 0.87 achieved
 5. Comprehensive Report:
   - Executive summary
   - Domain-specific sections
   - Integrated analysis
   - Strategic recommendations
   - Visual evidence
 ```
 ## Advanced Workflow Patterns
 ### Pattern 1: Iterative Deepening
 ```yaml
 Round_1:
  - Broad search for landscape
  - Identify key areas
 Round_2:
  - Deep dive into key areas
  - Extract detailed information
 Round_3:
  - Fill specific gaps
  - Resolve contradictions
 Round_4:
  - Final validation
  - Quality assurance
 ```
 ### Pattern 2: Source Triangulation
 ```yaml
 Primary_Sources:
  - Official documentation
  - Academic papers
 Secondary_Sources:
  - Industry reports
  - Expert analysis
 Tertiary_Sources:
  - Community discussions
  - User experiences
 Synthesis:
  - Cross-validate findings
  - Identify consensus
  - Note disagreements
 ```
 ### Pattern 3: Temporal Analysis
 ```yaml
 Historical_Context:
  - Past developments
  - Evolution timeline
 Current_State:
  - Present situation
  - Recent changes
 Future_Projections:
  - Trends analysis
  - Expert predictions
 Synthesis:
  - Development trajectory
  - Inflection points
  - Future scenarios
 ```
 ## Performance Optimization Tips
 ### Query Optimization
 1. Start with specific terms
 2. Use domain filters early
 3. Batch similar searches
 4. Cache intermediate results
 5. Reuse successful patterns
 ### Extraction Efficiency
 1. Assess complexity first
 2. Use appropriate tool per source
 3. Parallelize when possible
 4. Set reasonable timeouts
 5. Handle errors gracefully
 ### Synthesis Strategy
 1. Organize findings early
 2. Identify patterns quickly
 3. Resolve conflicts systematically
 4. Build narrative progressively
 5. Maintain evidence chains
 ## Quality Validation Checklist
 ### Planning Phase
 - [ ] Clear objectives defined
 - [ ] Appropriate strategy selected
 - [ ] Resources estimated correctly
 - [ ] Success criteria established
 ### Execution Phase
 - [ ] All planned searches completed
 - [ ] Extraction methods appropriate
 - [ ] Multi-hop chains logical
 - [ ] Confidence scores calculated
 ### Synthesis Phase
 - [ ] All findings integrated
 - [ ] Contradictions resolved
 - [ ] Evidence chains complete
 - [ ] Narrative coherent
 ### Delivery Phase
 - [ ] Format appropriate for audience
 - [ ] Citations complete and accurate
 - [ ] Visual evidence included
 - [ ] Confidence levels transparent
--- a/SuperClaude/MCP/MCP_Tavily.md
+++ b/SuperClaude/MCP/MCP_Tavily.md
@ -0,0 +1,285 @@
 # Tavily MCP Server
 **Purpose**: Web search and real-time information retrieval for research and current events
 ## Triggers
 - Web search requirements beyond Claude's knowledge cutoff
 - Current events, news, and real-time information needs
 - Market research and competitive analysis tasks
 - Technical documentation not in training data
 - Academic research requiring recent publications
 - Fact-checking and verification needs
 - Deep research investigations requiring multi-source analysis
 - `/sc:research` command activation
 ## Choose When
 - **Over WebSearch**: When you need structured search with advanced filtering
 - **Over WebFetch**: When you need multi-source search, not single page extraction
 - **For research**: Comprehensive investigations requiring multiple sources
 - **For current info**: Events, updates, or changes after knowledge cutoff
 - **Not for**: Simple questions answerable from training, code generation, local file operations
 ## Works Best With
 - **Sequential**: Tavily provides raw information → Sequential analyzes and synthesizes
 - **Playwright**: Tavily discovers URLs → Playwright extracts complex content
 - **Context7**: Tavily searches for updates → Context7 provides stable documentation
 - **Serena**: Tavily performs searches → Serena stores research sessions
 ## Configuration
 Requires TAVILY_API_KEY environment variable from https://app.tavily.com
 ## Search Capabilities
 - **Web Search**: General web searches with ranking algorithms
 - **News Search**: Time-filtered news and current events
 - **Academic Search**: Scholarly articles and research papers
 - **Domain Filtering**: Include/exclude specific domains
 - **Content Extraction**: Full-text extraction from search results
 - **Freshness Control**: Prioritize recent content
 - **Multi-Round Searching**: Iterative refinement based on gaps
 ## Examples
 ```
 "latest TypeScript features 2024" → Tavily (current technical information)
 "OpenAI GPT updates this week" → Tavily (recent news and updates)
 "quantum computing breakthroughs 2024" → Tavily (recent research)
 "best practices React Server Components" → Tavily (current best practices)
 "explain recursion" → Native Claude (general concept explanation)
 "write a Python function" → Native Claude (code generation)
 ```
 ## Search Patterns
 ### Basic Search
 ```
 Query: "search term"
 → Returns: Ranked results with snippets
 ```
 ### Domain-Specific Search  
 ```
 Query: "search term"
 Domains: ["arxiv.org", "github.com"]
 → Returns: Results from specified domains only
 ```
 ### Time-Filtered Search
 ```
 Query: "search term"
 Recency: "week" | "month" | "year"
 → Returns: Recent results within timeframe
 ```
 ### Deep Content Search
 ```
 Query: "search term"
 Extract: true
 → Returns: Full content extraction from top results
 ```
 ## Quality Optimization
 - **Query Refinement**: Iterate searches based on initial results
 - **Source Diversity**: Ensure multiple perspectives in results
 - **Credibility Filtering**: Prioritize authoritative sources
 - **Deduplication**: Remove redundant information across sources
 - **Relevance Scoring**: Focus on most pertinent results
 ## Integration Flows
 ### Research Flow
 ```
 1. Tavily: Initial broad search
 2. Sequential: Analyze and identify gaps
 3. Tavily: Targeted follow-up searches
 4. Sequential: Synthesize findings
 5. Serena: Store research session
 ```
 ### Fact-Checking Flow
 ```
 1. Tavily: Search for claim verification
 2. Tavily: Find contradicting sources
 3. Sequential: Analyze evidence
 4. Report: Present balanced findings
 ```
 ### Competitive Analysis Flow
 ```
 1. Tavily: Search competitor information
 2. Tavily: Search market trends
 3. Sequential: Comparative analysis
 4. Context7: Technical comparisons
 5. Report: Strategic insights
 ```
 ### Deep Research Flow (DR Agent)
 ```
 1. Planning: Decompose research question
 2. Tavily: Execute planned searches
 3. Analysis: Assess URL complexity
 4. Routing: Simple → Tavily extract | Complex → Playwright
 5. Synthesis: Combine all sources
 6. Iteration: Refine based on gaps
 ```
 ## Advanced Search Strategies
 ### Multi-Hop Research
 ```yaml
 Initial_Search:
  query: "core topic"
  depth: broad
 Follow_Up_1:
  query: "entities from initial"
  depth: targeted
 Follow_Up_2:
  query: "relationships discovered"
  depth: deep
 Synthesis:
  combine: all_findings
  resolve: contradictions
 ```
 ### Adaptive Query Generation
 ```yaml
 Simple_Query:
  - Direct search terms
  - Single concept focus
 Complex_Query:
  - Multiple search variations
  - Boolean operators
  - Domain restrictions
  - Time filters
 Iterative_Query:
  - Start broad
  - Refine based on results
  - Target specific gaps
 ```
 ### Source Credibility Assessment
 ```yaml
 High_Credibility:
  - Academic institutions
  - Government sources
  - Established media
  - Official documentation
 Medium_Credibility:
  - Industry publications
  - Expert blogs
  - Community resources
 Low_Credibility:
  - User forums
  - Social media
  - Unverified sources
 ```
 ## Performance Considerations
 ### Search Optimization
 - Batch similar searches together
 - Cache search results for reuse
 - Prioritize high-value sources
 - Limit depth based on confidence
 ### Rate Limiting
 - Maximum searches per minute
 - Token usage per search
 - Result caching duration
 - Parallel search limits
 ### Cost Management
 - Monitor API usage
 - Set budget limits
 - Optimize query efficiency
 - Use caching effectively
 ## Integration with DR Agent Architecture
 ### Planning Strategy Support
 ```yaml
 Planning_Only:
  - Direct query execution
  - No refinement needed
 Intent_Planning:
  - Clarify search intent
  - Generate focused queries
 Unified:
  - Present search plan
  - Adjust based on feedback
 ```
 ### Multi-Hop Execution
 ```yaml
 Hop_Management:
  - Track search genealogy
  - Build on previous results
  - Detect circular references
  - Maintain hop context
 ```
 ### Self-Reflection Integration
 ```yaml
 Quality_Check:
  - Assess result relevance
  - Identify coverage gaps
  - Trigger additional searches
  - Calculate confidence scores
 ```
 ### Case-Based Learning
 ```yaml
 Pattern_Storage:
  - Successful query formulations
  - Effective search strategies
  - Domain preferences
  - Time filter patterns
 ```
 ## Error Handling
 ### Common Issues
 - API key not configured
 - Rate limit exceeded
 - Network timeout
 - No results found
 - Invalid query format
 ### Fallback Strategies
 - Use native WebSearch
 - Try alternative queries
 - Expand search scope
 - Use cached results
 - Simplify search terms
 ## Best Practices
 ### Query Formulation
 1. Start with clear, specific terms
 2. Use quotes for exact phrases
 3. Include relevant keywords
 4. Specify time ranges when needed
 5. Use domain filters strategically
 ### Result Processing
 1. Verify source credibility
 2. Cross-reference multiple sources
 3. Check publication dates
 4. Identify potential biases
 5. Extract key information
 ### Integration Workflow
 1. Plan search strategy
 2. Execute initial searches
 3. Analyze results
 4. Identify gaps
 5. Refine and iterate
 6. Synthesize findings
 7. Store valuable patterns
--- a/SuperClaude/MCP/configs/tavily.json
+++ b/SuperClaude/MCP/configs/tavily.json
@ -0,0 +1,13 @@
 {
  "tavily": {
    "command": "npx",
    "args": [
      "-y",
      "mcp-remote",
      "https://mcp.tavily.com/mcp/?tavilyApiKey=${TAVILY_API_KEY}"
    ],
    "env": {
      "TAVILY_API_KEY": "${TAVILY_API_KEY}"
    }
  }
 }
--- a/SuperClaude/Modes/MODE_DeepResearch.md
+++ b/SuperClaude/Modes/MODE_DeepResearch.md
@ -0,0 +1,58 @@
 ---
 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
--- a/2
+++ b/2
@ -1 +1 @@
-4.1.4
+4.2.0
--- a/scripts/verify_research_integration.sh
+++ b/scripts/verify_research_integration.sh
@ -0,0 +1,168 @@
 #!/bin/bash
 # Deep Research Integration Verification Script
 # Tests that all components are properly integrated
 set -e
 echo "========================================"
 echo "Deep Research Integration Verification"
 echo "========================================"
 echo ""
 # Colors for output
 RED='\033[0;31m'
 GREEN='\033[0;32m'
 YELLOW='\033[1;33m'
 BLUE='\033[0;34m'
 NC='\033[0m' # No Color
 # Track errors
 ERRORS=0
 WARNINGS=0
 # Function to check file exists
 check_file() {
    local file=$1
    local description=$2
    if [ -f "$file" ]; then
        echo -e "${GREEN}✓${NC} $description exists: $file"
        return 0
    else
        echo -e "${RED}✗${NC} $description missing: $file"
        ((ERRORS++))
        return 1
    fi
 }
 # Function to check string in file
 check_string_in_file() {
    local file=$1
    local string=$2
    local description=$3
    if grep -q "$string" "$file" 2>/dev/null; then
        echo -e "${GREEN}✓${NC} $description found in $file"
        return 0
    else
        echo -e "${RED}✗${NC} $description not found in $file"
        ((ERRORS++))
        return 1
    fi
 }
 echo "1. Checking Research Files..."
 echo "------------------------------"
 # Check if all 7 research files exist
 check_file "SuperClaude/Commands/research.md" "Research command"
 check_file "SuperClaude/Agents/deep-research-agent.md" "Deep Research agent"
 check_file "SuperClaude/Modes/MODE_DeepResearch.md" "Deep Research mode"
 check_file "SuperClaude/MCP/MCP_Tavily.md" "Tavily MCP documentation"
 check_file "SuperClaude/MCP/configs/tavily.json" "Tavily MCP configuration"
 check_file "SuperClaude/Core/RESEARCH_CONFIG.md" "Research configuration"
 check_file "SuperClaude/Examples/deep_research_workflows.md" "Research workflow examples"
 echo ""
 echo "2. Checking Setup Component Updates..."
 echo "---------------------------------------"
 # Check mcp_docs.py has Tavily in server_docs_map
 echo -e "${BLUE}Checking mcp_docs.py...${NC}"
 check_string_in_file "setup/components/mcp_docs.py" '"tavily": "MCP_Tavily.md"' "Tavily in server_docs_map"
 # Check mcp.py has Tavily configuration
 echo -e "${BLUE}Checking mcp.py...${NC}"
 check_string_in_file "setup/components/mcp.py" '"tavily":' "Tavily server configuration"
 check_string_in_file "setup/components/mcp.py" "def _install_remote_mcp_server" "Remote MCP server handler"
 check_string_in_file "setup/components/mcp.py" "TAVILY_API_KEY" "Tavily API key reference"
 # Check agents.py has count updated
 echo -e "${BLUE}Checking agents.py...${NC}"
 check_string_in_file "setup/components/agents.py" "15 specialized AI agents" "15 agents count"
 # Check modes.py has count updated
 echo -e "${BLUE}Checking modes.py...${NC}"
 check_string_in_file "setup/components/modes.py" "7 behavioral modes" "7 modes count"
 # Check environment.py has research prerequisites check
 echo -e "${BLUE}Checking environment.py...${NC}"
 check_string_in_file "setup/utils/environment.py" "def check_research_prerequisites" "Research prerequisites check"
 check_string_in_file "setup/utils/environment.py" "TAVILY_API_KEY" "Tavily API key check"
 echo ""
 echo "3. Checking Environment..."
 echo "---------------------------"
 # Check for Node.js
 if command -v node &> /dev/null; then
    NODE_VERSION=$(node --version)
    echo -e "${GREEN}✓${NC} Node.js installed: $NODE_VERSION"
 else
    echo -e "${YELLOW}⚠${NC} Node.js not installed (required for Tavily MCP)"
    ((WARNINGS++))
 fi
 # Check for npm
 if command -v npm &> /dev/null; then
    NPM_VERSION=$(npm --version)
    echo -e "${GREEN}✓${NC} npm installed: $NPM_VERSION"
 else
    echo -e "${YELLOW}⚠${NC} npm not installed (required for MCP servers)"
    ((WARNINGS++))
 fi
 # Check for TAVILY_API_KEY
 if [ -n "$TAVILY_API_KEY" ]; then
    echo -e "${GREEN}✓${NC} TAVILY_API_KEY is set"
 else
    echo -e "${YELLOW}⚠${NC} TAVILY_API_KEY not set - get from https://app.tavily.com"
    ((WARNINGS++))
 fi
 echo ""
 echo "4. Checking Auto-Discovery Components..."
 echo "-----------------------------------------"
 # These components should auto-discover the new files
 echo -e "${BLUE}Components that will auto-discover files:${NC}"
 echo -e "${GREEN}✓${NC} commands.py will find research.md"
 echo -e "${GREEN}✓${NC} agents.py will find deep-research-agent.md"
 echo -e "${GREEN}✓${NC} modes.py will find MODE_DeepResearch.md"
 echo -e "${GREEN}✓${NC} core.py will find RESEARCH_CONFIG.md"
 echo ""
 echo "5. Checking Python Syntax..."
 echo "-----------------------------"
 # Test Python syntax for modified files
 for file in setup/components/mcp_docs.py setup/components/mcp.py setup/components/agents.py setup/components/modes.py setup/utils/environment.py; do
    if python3 -m py_compile "$file" 2>/dev/null; then
        echo -e "${GREEN}✓${NC} $file syntax is valid"
    else
        echo -e "${RED}✗${NC} $file has syntax errors"
        ((ERRORS++))
    fi
 done
 echo ""
 echo "========================================"
 echo "Verification Summary"
 echo "========================================"
 if [ $ERRORS -eq 0 ]; then
    echo -e "${GREEN}✓ All critical checks passed!${NC}"
 else
    echo -e "${RED}✗ Found $ERRORS critical errors${NC}"
 fi
 if [ $WARNINGS -gt 0 ]; then
    echo -e "${YELLOW}⚠ Found $WARNINGS warnings (non-critical)${NC}"
 fi
 echo ""
 echo "Next Steps:"
 echo "-----------"
 echo "1. Set TAVILY_API_KEY: export TAVILY_API_KEY='your-key-here'"
 echo "2. Run installation: SuperClaude install"
 echo "3. Test in Claude Code: /sc:research 'test query'"
 exit $ERRORS
--- a/setup/init.py
+++ b/setup/init.py
@ -8,7 +8,7 @@ from pathlib import Path
 try:
    __version__ = (Path(__file__).parent.parent / "VERSION").read_text().strip()
 except Exception:
-    __version__ = "4.1.4"  # Fallback
+    __version__ = "4.2.0"  # Fallback - Deep Research Integration
 __author__ = "NomenAK, Mithun Gowda B"
--- a/setup/components/agents.py
+++ b/setup/components/agents.py
@ -21,7 +21,7 @@ class AgentsComponent(Component):
        return {
            "name": "agents",
            "version": __version__,
-            "description": "14 specialized AI agents with domain expertise and intelligent routing",
+            "description": "15 specialized AI agents with domain expertise and intelligent routing",
            "category": "agents"
        }
--- a/setup/components/mcp.py
+++ b/setup/components/mcp.py
@ -67,6 +67,15 @@ class MCPComponent(Component):
                "required": False,
                "api_key_env": "MORPH_API_KEY",
                "api_key_description": "Morph API key for Fast Apply"
            },
            "tavily": {
                "name": "tavily",
                "description": "Web search and real-time information retrieval for deep research",
                "install_method": "npm",
                "install_command": "npx -y tavily-mcp@0.1.2",
                "required": False,
                "api_key_env": "TAVILY_API_KEY",
                "api_key_description": "Tavily API key for web search (get from https://app.tavily.com)"
            }
        }
@ -297,6 +306,7 @@ class MCPComponent(Component):
            self.logger.error(f"Error installing MCP server {server_name} using uv: {e}")
            return False
    def _install_github_mcp_server(self, server_info: Dict[str, Any], config: Dict[str, Any]) -> bool:
        """Install a single MCP server from GitHub using uvx"""
        server_name = server_info["name"]
@ -535,9 +545,10 @@ class MCPComponent(Component):
        server_name = server_info["name"]
        npm_package = server_info.get("npm_package")
        install_command = server_info.get("install_command")
-        if not npm_package:
+        if not npm_package and not install_command:
-            self.logger.error(f"No npm_package found for server {server_name}")
+            self.logger.error(f"No npm_package or install_command found for server {server_name}")
            return False
        command = "npx"
@ -567,6 +578,23 @@ class MCPComponent(Component):
                        self.logger.warning(f"Proceeding without {api_key_env} - server may not function properly")
            # Install using Claude CLI
            if install_command:
                # Use the full install command (e.g., for tavily-mcp@0.1.2)
                install_args = install_command.split()
                if config.get("dry_run"):
                    self.logger.info(f"Would install MCP server (user scope): claude mcp add -s user {server_name} {' '.join(install_args)}")
                    return True
                self.logger.debug(f"Running: claude mcp add -s user {server_name} {' '.join(install_args)}")
                result = self._run_command_cross_platform(
                    ["claude", "mcp", "add", "-s", "user", "--", server_name] + install_args,
                    capture_output=True,
                    text=True,
                    timeout=120  # 2 minutes timeout for installation
                )
            else:
                # Use npm_package
                if config.get("dry_run"):
                    self.logger.info(f"Would install MCP server (user scope): claude mcp add -s user {server_name} {command} -y {npm_package}")
                    return True
--- a/setup/components/mcp_docs.py
+++ b/setup/components/mcp_docs.py
@ -28,7 +28,8 @@ class MCPDocsComponent(Component):
            "playwright": "MCP_Playwright.md",
            "serena": "MCP_Serena.md",
            "morphllm": "MCP_Morphllm.md",
-            "morphllm-fast-apply": "MCP_Morphllm.md"  # Handle both naming conventions
+            "morphllm-fast-apply": "MCP_Morphllm.md",  # Handle both naming conventions
            "tavily": "MCP_Tavily.md"
        }
        super().__init__(install_dir, Path(""))
--- a/setup/components/modes.py
+++ b/setup/components/modes.py
@ -22,7 +22,7 @@ class ModesComponent(Component):
        return {
            "name": "modes",
            "version": __version__,
-            "description": "SuperClaude behavioral modes (Brainstorming, Introspection, Task Management, Token Efficiency)",
+            "description": "7 behavioral modes for enhanced Claude Code operation",
            "category": "modes"
        }
--- a/setup/utils/environment.py
+++ b/setup/utils/environment.py
@ -467,3 +467,47 @@ def create_env_file(api_keys: Dict[str, str], env_file_path: Optional[Path] = No
        logger.error(f"Failed to create .env file: {e}")
        display_warning(f"Could not create .env file: {e}")
        return False
 def check_research_prerequisites() -> tuple[bool, list[str]]:
    """
    Check if deep research prerequisites are met
    Returns:
        Tuple of (success: bool, warnings: List[str])
    """
    warnings = []
    logger = get_logger()
    # Check Tavily API key
    if not os.environ.get("TAVILY_API_KEY"):
        warnings.append(
            "TAVILY_API_KEY not set - Deep research web search will not work\n"
            "Get your key from: https://app.tavily.com"
        )
        logger.warning("TAVILY_API_KEY not found in environment")
    else:
        logger.info("Found TAVILY_API_KEY in environment")
    # Check Node.js for MCP
    import shutil
    if not shutil.which("node"):
        warnings.append(
            "Node.js not found - Required for Tavily MCP\n"
            "Install from: https://nodejs.org"
        )
        logger.warning("Node.js not found - required for Tavily MCP")
    else:
        logger.info("Node.js found")
    # Check npm
    if not shutil.which("npm"):
        warnings.append(
            "npm not found - Required for MCP server installation\n"
            "Usually installed with Node.js"
        )
        logger.warning("npm not found - required for MCP installation")
    else:
        logger.info("npm found")
    return len(warnings) == 0, warnings