deep-research
Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings
Best for complex investigationsWorks with GitHubLow risk
⌘source
- author
@ruvnet- repo
- ruvnet/ruflo
- language
- TypeScript
★46kstars
5.1kforks
May 5, 2026
✦overview.md
Key Features
- ·Multi-phase research orchestration
- ·Web search and fetch integration
- ·Memory and pattern retrieval
- ·Structured synthesis output
Use Cases
- →Investigating a new framework or library
- →Gathering requirements for a complex feature
- →Cross-referencing internal docs with external sources
Best for
- ✓complex investigations
- ✓research-heavy tasks
Not ideal for
- !quick lookups
- !simple fact-checking
FAQs
name
deep-research
description
Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings
argument-hint:<topic>
allowed-tools:mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__memory_search_unified mcp__claude-flow__agentdb_hierarchical-store mcp__claude-flow__agentdb_hierarchical-recall mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__agentdb_pattern-store mcp__claude-flow__neural_predict mcp__claude-flow__hooks_intelligence_pattern-search mcp__claude-flow__hooks_intelligence_pattern-store mcp__claude-flow__task_create mcp__claude-flow__task_list mcp__claude-flow__task_summary Bash WebSearch WebFetch Read Write
Deep Research
Orchestrate multi-phase deep research campaigns that gather, cross-reference, and synthesize information from multiple sources.
When to use
When you need to investigate a complex topic thoroughly — spanning web sources, codebase patterns, stored memory, and external documentation — and produce a structured synthesis.
Steps
- Define research scope — break the question into 3-7 sub-questions that together answer the main question
- Search existing knowledge — call
mcp__claude-flow__memory_search_unifiedandmcp__claude-flow__agentdb_pattern-searchto check what's already known - Web research — use
WebSearchandWebFetchto gather external information for each sub-question - Codebase analysis — use
Bash(grep/find),Readto examine relevant source files - Cross-reference — compare findings across sources, identify agreements and contradictions
- Store findings — call
mcp__claude-flow__memory_storewith namespaceresearchfor each key finding - Store patterns — call
mcp__claude-flow__agentdb_pattern-storefor reusable patterns discovered - Synthesize — produce a structured research report with:
$install
1-click copynpx skills add ruvnet/ruflo --skill deep-researchSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
4/ 5
highMostly actionable with clear steps; only a few small gaps remain.