SuperClaude/plugins/superclaude/examples/deep_research_workflows.md

Deep Research Workflows

Example 1: Planning-Only Strategy

Scenario

Clear research question: "Latest TensorFlow 3.0 features"

Execution

/sc:research "Latest TensorFlow 3.0 features" --strategy planning-only --depth standard

Workflow

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

/sc:research "AI safety" --strategy intent-planning --depth deep

Workflow

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

/sc:research "Build AI startup competitive analysis" --strategy unified --hops 5

Workflow

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

/sc:research "Rust async runtime comparison" --memory enabled

Workflow

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

/sc:research "Quantum computing for optimization" --confidence 0.8 --depth exhaustive

Workflow

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

/sc:research "Next.js 14 App Router complete guide" --depth deep --scrape selective --screenshots

Workflow

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

/sc:research "AI writing assistant tools pricing features 2024" --scrape all --screenshots --interactive

Workflow

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

/sc:research "transformer architecture improvements 2024" --depth exhaustive --auth --scrape auto

Workflow

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

/sc:research "cryptocurrency market analysis BTC ETH 2024" --scrape all --interactive --screenshots

Workflow

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

/sc:research "AI in healthcare applications 2024" --depth exhaustive --hops 5 --parallel

Workflow

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

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

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

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