nguyenmanhtuan2004/Deep Research

Deep Research Skill

Perform thorough research using web search, documentation, and intelligent synthesis to inform development decisions.

Capabilities

• Web search via WebSearch tool
• Documentation analysis
• Technology trend research
• Competitive analysis
• Best practices discovery
• Academic/technical paper review

Research Methodology

Phase 1: Broad Search (10-15 sources)

• Query multiple search engines
• Scan for credibility (check date, author, domain)
• Filter by relevance score
• Prioritize official docs, established blogs, GitHub repos

Phase 2: Deep Dive (Top 5 sources)

• Read thoroughly
• Extract key insights
• Identify patterns and trends
• Note contradictions or debates
• Look for code examples and real-world applications

Phase 3: Synthesis

• Combine findings into cohesive narrative
• Create actionable recommendations
• Document all sources
• Generate summary report

Output Format

Research saved to: temp/research/{topic}-{timestamp}.md

# Research: {Topic}

## Executive Summary
[3-5 bullet points - key findings]

## Key Findings

### 1. {Finding Title}
- **Source**: [Link](url)
- **Insight**: What was learned
- **Actionable**: How to apply this
- **Code Example**: (if applicable)

### 2. {Finding Title}
...

## Recommendations
1. **Immediate Action**: What to do now
2. **Best Practice**: Pattern to follow
3. **Avoid**: What not to do

## Implementation Plan
- [ ] Step 1
- [ ] Step 2

## Sources
- [Title](URL) - Brief description
- [Title](URL) - Brief description

Usage Examples

Technology Research

"deep research on LangGraph supervisor pattern for production systems
 Focus on: state management, error handling, scalability
 Save to: temp/research/langgraph-supervisor.md"

Competitive Analysis

"research competitors in AI code generation space
 Analyze: features, pricing, tech stack, user feedback
 Identify: gaps we can fill, unique angles
 Output: temp/research/competitive-analysis.md"

Best Practices

"research React Server Components best practices for Next.js 14
 Include: when to use vs client components, data fetching patterns, common pitfalls
 Find: code examples from Vercel and community
 Save: temp/research/rsc-best-practices.md"

Integration with Build Process

Research findings automatically:

1. Update Learning: Add insights to directives/learning.json
2. Create Specs: If features found → add to backlog
3. Improve Docs: Suggest updates to INSTRUCTIONS.md
4. Inform Architecture: Use findings in technical decisions

Research Quality Checklist

Before completing research:

• [ ] At least 5 credible sources
• [ ] Checked for recency (prefer <1 year old info)
• [ ] Included official documentation
• [ ] Found real-world examples/code
• [ ] Synthesized conflicting information
• [ ] Created actionable recommendations
• [ ] Documented all sources with working links

Advanced Research Patterns

Comparative Research

"research and compare:
 Option A: Using Prisma ORM
 Option B: Using raw SQL with Postgres
 Option C: Using Drizzle ORM

 Compare: performance, DX, type safety, migrations, community support
 Recommend: Best option for Next.js 14 + Supabase stack"

Trend Analysis

"research current trends in AI agent orchestration frameworks
 Analyze: LangGraph, CrewAI, AutoGPT, LangChain, Semantic Kernel
 Identify: Which is gaining traction, production-ready, best for SaaS
 Timeline: Last 6 months only"

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Remember: Great research leads to better decisions. Invest time in deep research before implementation!