137 lines
2.9 KiB
Markdown
137 lines
2.9 KiB
Markdown
# Research Swarm Strategy
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## Purpose
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Deep research through parallel information gathering.
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## Activation
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### Using MCP Tools
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```javascript
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// Initialize research swarm
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mcp__claude-flow__swarm_init({
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"topology": "mesh",
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"maxAgents": 6,
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"strategy": "adaptive"
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})
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// Orchestrate research task
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mcp__claude-flow__task_orchestrate({
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"task": "research topic X",
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"strategy": "parallel",
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"priority": "medium"
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})
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```
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### Using CLI (Fallback)
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`npx claude-flow swarm "research topic X" --strategy research`
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## Agent Roles
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### Agent Spawning with MCP
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```javascript
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// Spawn research agents
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mcp__claude-flow__agent_spawn({
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"type": "researcher",
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"name": "Web Researcher",
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"capabilities": ["web-search", "content-extraction", "source-validation"]
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})
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mcp__claude-flow__agent_spawn({
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"type": "researcher",
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"name": "Academic Researcher",
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"capabilities": ["paper-analysis", "citation-tracking", "literature-review"]
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})
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mcp__claude-flow__agent_spawn({
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"type": "analyst",
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"name": "Data Analyst",
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"capabilities": ["data-processing", "statistical-analysis", "visualization"]
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})
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mcp__claude-flow__agent_spawn({
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"type": "documenter",
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"name": "Report Writer",
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"capabilities": ["synthesis", "technical-writing", "formatting"]
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})
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```
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## Research Methods
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### Information Gathering
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```javascript
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// Parallel information collection
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mcp__claude-flow__parallel_execute({
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"tasks": [
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{ "id": "web-search", "command": "search recent publications" },
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{ "id": "academic-search", "command": "search academic databases" },
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{ "id": "data-collection", "command": "gather relevant datasets" }
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]
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})
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// Store research findings
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mcp__claude-flow__memory_usage({
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"action": "store",
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"key": "research-findings-" + Date.now(),
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"value": JSON.stringify(findings),
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"namespace": "research",
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"ttl": 604800 // 7 days
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})
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```
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### Analysis and Validation
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```javascript
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// Pattern recognition in findings
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mcp__claude-flow__pattern_recognize({
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"data": researchData,
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"patterns": ["trend", "correlation", "outlier"]
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})
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// Cognitive analysis
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mcp__claude-flow__cognitive_analyze({
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"behavior": "research-synthesis"
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})
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// Cross-reference validation
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mcp__claude-flow__quality_assess({
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"target": "research-sources",
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"criteria": ["credibility", "relevance", "recency"]
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})
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```
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### Knowledge Management
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```javascript
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// Search existing knowledge
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mcp__claude-flow__memory_search({
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"pattern": "topic X",
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"namespace": "research",
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"limit": 20
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})
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// Create knowledge connections
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mcp__claude-flow__neural_patterns({
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"action": "learn",
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"operation": "knowledge-graph",
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"metadata": {
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"topic": "X",
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"connections": relatedTopics
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}
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})
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```
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### Reporting
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```javascript
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// Generate research report
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mcp__claude-flow__workflow_execute({
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"workflowId": "research-report-generation",
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"params": {
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"findings": findings,
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"format": "comprehensive"
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}
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})
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// Monitor progress
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mcp__claude-flow__swarm_status({
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"swarmId": "research-swarm"
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})
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```
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