/f5-research

description: Deep research with web search and documentation
argument-hint: <topic> [--depth quick|deep|exhaustive]
mcp-servers: tavily, context7

/f5-research - Deep Research

Conduct deep research with AI agents and multi-source synthesis.

ARGUMENTS

The user's request is: $ARGUMENTS

PURPOSE

Deep research for complex investigations:

• Multi-source information gathering
• Evidence synthesis and validation
• Structured analysis and reporting
• Quality gate D1 (Research Complete) support

RESEARCH CAPABILITIES

| Capability | Description | Tools Used | |------------|-------------|------------| | Web Search | Current information from web | Tavily | | Documentation | Official library/framework docs | Context7 | | Code Analysis | Codebase investigation | Serena, Grep | | Reasoning | Complex multi-step analysis | Sequential | | Validation | Cross-reference and verify | Multiple |

ACTIONS

Start Research Session

/f5-research "React Server Components best practices"

Output:
🔍 Starting Deep Research Session

Topic: React Server Components best practices

Planning research strategy...

Research Plan:
1. Search for official React documentation
2. Find community best practices (2024)
3. Identify common patterns and anti-patterns
4. Gather performance considerations
5. Synthesize findings

Estimated sources: 10-15
Estimated time: 2-3 minutes

Beginning research...

Research with Specific Focus

/f5-research "authentication patterns" --focus security

Output:
🔍 Deep Research: Authentication Patterns

Focus Area: Security

Research Plan:
1. Search for OWASP authentication guidelines
2. Find JWT security best practices
3. Research session management patterns
4. Identify common vulnerabilities
5. Gather mitigation strategies

Security-focused sources:
• OWASP guidelines
• CVE databases
• Security advisories
• Penetration testing reports

Beginning security-focused research...

Research for Requirements (Gate D1)

/f5-research "user authentication requirements" --gate D1

Output:
🔍 Requirements Research (Gate D1)

Topic: User Authentication Requirements

Research aligned with Gate D1 criteria:
• Evidence count target: ≥3 sources
• Quality score target: ≥80%

Research Plan:
1. Industry standards review
2. Competitor analysis
3. User expectations research
4. Security requirements
5. Compliance requirements (if applicable)

Progress tracking:
  Sources found: 0/3 minimum
  Quality score: Calculating...

Beginning D1-focused research...

Research Output

═══════════════════════════════════════════════════════════
📊 RESEARCH REPORT: React Server Components Best Practices
═══════════════════════════════════════════════════════════

## Executive Summary

React Server Components (RSC) represent a paradigm shift in
React development, enabling server-side rendering of components
with zero client-side JavaScript bundle impact.

## Key Findings

### 1. When to Use Server Components
**Evidence: 4 sources | Confidence: High**

• Default to Server Components for:
  - Data fetching
  - Backend resource access
  - Static content
  - Large dependencies

• Use Client Components for:
  - Interactivity (onClick, onChange)
  - Browser APIs
  - State management
  - Effects (useEffect)

### 2. Best Practices
**Evidence: 6 sources | Confidence: High**

1. **Composition Pattern**
   Wrap client components in server components for data fetching

2. **Serialization Boundary**
   Only serializable props can cross server/client boundary

3. **Streaming**
   Use Suspense for progressive rendering

### 3. Common Anti-Patterns
**Evidence: 3 sources | Confidence: Medium**

• ❌ Using 'use client' unnecessarily
• ❌ Passing non-serializable props
• ❌ Ignoring streaming opportunities
• ❌ Over-fetching data

### 4. Performance Considerations
**Evidence: 5 sources | Confidence: High**

• Reduced JavaScript bundle size
• Faster initial page load
• Better SEO capabilities
• Server-side caching benefits

## Sources

1. React Official Documentation (react.dev) - High credibility
2. Next.js Documentation (nextjs.org) - High credibility
3. Vercel Blog - Medium credibility
4. Community discussions (GitHub) - Medium credibility

## Quality Metrics

| Metric | Score |
|--------|-------|
| Source diversity | 85% |
| Evidence coverage | 90% |
| Confidence level | High |
| Overall quality | 88% |

## Recommendations

1. Start with Server Components as default
2. Only add 'use client' when necessary
3. Use composition for optimal performance
4. Implement streaming with Suspense

═══════════════════════════════════════════════════════════
Research completed | 12 sources analyzed | Quality: 88%
═══════════════════════════════════════════════════════════

RESEARCH MODES

Quick Research

/f5-research "topic" --quick

• 3-5 sources
• 1-2 minutes
• Key findings only
• Good for fact-checking

Standard Research

/f5-research "topic"

• 8-12 sources
• 3-5 minutes
• Full analysis
• Default mode

Deep Research

/f5-research "topic" --deep

• 15-20+ sources
• 5-10 minutes
• Comprehensive analysis
• Multiple perspectives
• Contradiction resolution

Exhaustive Research

/f5-research "topic" --exhaustive

• 25+ sources
• 10-15 minutes
• Academic-level depth
• Full citation chain
• Expert synthesis

RESEARCH STRATEGIES

By Topic Type

| Topic Type | Strategy | Primary Tools | |------------|----------|---------------| | Technical | Docs + Code | Context7, Serena | | Current Events | Web Search | Tavily | | Best Practices | Multi-source | Tavily, Context7 | | Architecture | Analysis | Sequential, Serena | | Security | Specialized | Tavily (OWASP focus) |

Multi-Hop Research

/f5-research "microservices communication patterns" --multi-hop

Research Flow:
  Hop 1: Overview of patterns
    → Found: REST, gRPC, Message Queues, Event Sourcing

  Hop 2: Deep dive each pattern
    → REST: 5 sources
    → gRPC: 4 sources
    → Message Queu