Ultimate Research - Elite Multi-Agent Research Orchestration

Core Capability

This skill orchestrates the most comprehensive research capability available by:

• Parallel agent execution: Simultaneously deploy all 4 specialized research agents
• Full MCP utilization: Leverage every tool from ref, exa, brave-search, and context7 MCPs
• Best practices adherence: Follow MCP_BEST_PRACTICES_COMPREHENSIVE_GUIDE.md for optimal performance
• Sequential thinking: Use complex reasoning with sequential-thinking MCP
• Token efficiency: 96% reduction via smart routing and caching

When This Skill Activates

Automatically triggers during /sc:research commands or when:

• User requests comprehensive research
• Multi-dimensional analysis needed
• Current events + historical context required
• Code examples + documentation + community insights needed
• Competitive analysis requiring multiple perspectives
• Academic/technical research with citation requirements

Research Architecture

Tier 1: Parallel Agent Deployment

Execute simultaneously for maximum efficiency:

┌─────────────────────────────────────────┐
│     Research Query Received             │
└──────────────┬──────────────────────────┘
               │
     ┌─────────┼──────────┬──────────┐
     │         │          │          │
     ▼         ▼          ▼          ▼
┌─────────┐ ┌──────┐ ┌────────┐ ┌────────┐
│ deep-   │ │search│ │ trend- │ │  ux-   │
│research │ │spec  │ │research│ │research│
│ -agent  │ │      │ │  -er   │ │  -er   │
└─────────┘ └──────┘ └────────┘ └────────┘
     │         │          │          │
     └─────────┴──────────┴──────────┘
               │
               ▼
     ┌────────────────────┐
     │   Synthesis &      │
     │   Validation       │
     └────────────────────┘

Agent Responsibilities:

1. deep-research-agent: Complex multi-hop analysis, evidence chains, synthesis
2. search-specialist: Web search, documentation discovery, source verification
3. trend-researcher: Market trends, viral content, emerging patterns
4. ux-researcher: User behavior, pain points, feedback analysis

Tier 2: MCP Tool Orchestration

Use decision tree based on MCP_BEST_PRACTICES_COMPREHENSIVE_GUIDE.md:

Documentation Queries:

Primary: ref MCP → Fast, token-efficient (96% reduction)
Fallback: exa MCP → Code context if docs insufficient
Tertiary: brave-search MCP → General web if needed

Code Example Queries:

Primary: exa MCP (get_code_context_exa) → Semantic search, fastest (1.18s)
Fallback: ref MCP → Official documentation
Tertiary: brave-search MCP → Tutorials and guides

News/Current Events:

Primary: brave-search MCP (news_search, freshness='pd') → Latest information
Secondary: exa MCP → Deep analysis
Verification: context7 MCP → Documentation updates

Research/Semantic:

Primary: exa MCP (web_search_exa) → AI-native search
Secondary: brave-search MCP → Broad coverage
Documentation: ref MCP + context7 MCP → Technical verification

Tier 3: Sequential Thinking Integration

For complex reasoning tasks, invoke sequential-thinking MCP:

When to use:
- Multi-step logical deduction needed
- Conflicting information requires resolution
- Hypothesis generation and validation
- Cost-benefit analysis across MCPs
- Quality vs speed tradeoffs

Execution Protocol

Phase 1: Parallel Agent Launch (0-30 seconds)

Launch all 4 agents simultaneously in single message:

[Task tool invocation with 4 parallel calls]

deep-research-agent task: [research scope]
search-specialist task: [documentation discovery]
trend-researcher task: [market analysis]
ux-researcher task: [user insights]

Critical: Use single message with multiple Task tool calls for true parallelism

Phase 2: MCP Tool Utilization (30s - 5min)

Based on agent requirements, execute MCP tools following best practices:

Token Optimization:

- ref MCP: Use for all documentation (automatic 5k token limit)
- exa MCP: Configure tokensNum=8000 for code research
- brave-search MCP: Set count=10, result_filter=["web","news"]
- context7 MCP: Use mode='code' for API refs, mode='info' for guides

Parallel Execution Example:

# Execute independent searches simultaneously
await asyncio.gather(
    ref_search_documentation("React hooks TypeScript"),
    exa_code_context("React hooks examples", tokensNum=8000),
    brave_web_search("React hooks best practices 2024"),
    context7_get_library_docs("/facebook/react", topic="hooks")
)

Caching Strategy (from MCP best practices):

Documentation (ref): 7-day TTL
Code examples (exa): 1-day TTL
News (brave): 1-hour TTL
General web: 6-hour TTL

Phase 3: Sequential Thinking (as needed)

When complex reasoning required:

Use sequential-thinking MCP to:
1. Analyze conflicting sources
2. Score result relevance
3. Identify information gaps
4. Generate synthesis strategy
5. Validate conclusions

Phase 4: Synthesis & Validation (final 20%)

Merge findings from all agents and MCPs:

1. Deduplicate results (by URL/content hash)
2. Score relevance (0-100 scale)
3. Rank by authority + freshness + completeness
4. Cross-verify claims across sources
5. Generate confidence scores
6. Build evidence chains

MCP Best Practices Implementation

From MCP_BEST_PRACTICES_COMPREHENSIVE_GUIDE.md

Performance Optimization:

• Enable only needed Exa tools: --tools=get_code_context_exa,web_search_exa
• Use Ref HTTP method (fastest setup)
• Implement request queuing for rate limits
• Progressive loading: summaries first, details on-demand

Cost Optimization:

• Budget-aware routing (see guide Section 7.3)
• Aggressive caching (60-80% hit rate achievable)
• Token limit tuning per query type
• Free tier maximization strategy

Quality Optimization:

• Result scoring algorithm (relevance + freshness + authority + completeness)
• Confidence thresholds by query type
• Fallback chains for reliability
• Multi-MCP verification for critical info

Output Format

Research Report Structure

# [Research Topic]

**Research Date**: YYYY-MM-DD
**Confidence Score**: XX/100
**Sources Consulted**: N unique sources

## Executive Summary
[3-5 sentence overview of key findings]

## Key Findings

### 1. [Finding Category]
- **Source**: [Agent + MCP used]
- **Evidence**: [Supporting data]
- **Confidence**: [High/Medium/Low]
- **Citations**: [URLs/references]

### 2. [Finding Category]
...

## Detailed Analysis

### [Section 1]: Official Documentation
**Source**: ref MCP + context7 MCP
[Findings from authoritative sources]

### [Section 2]: Code Examples & Implementations
**Source**: exa MCP
[Real-world usage patterns]

### [Section 3]: Community Insights
**Source**: brave-search MCP + search-specialist agent
[Developer feedback, common issues]

### [Section 4]: Market Trends
**Source**: trend-researcher agent + exa MCP
[Current state, emerging patterns]

### [Section 5]: User Experience Analysis
**Source**: ux-researcher agent
[Pain points, opportunities]

## Synthesis & Recommendations

### Best Practices Identified
1. [Practice 1] - Source: [agents/MCPs]
2. [Practice 2] - Source: [agents/MCPs]

### Common Pitfalls
1. [Pitfall 1] - Source: [agents/MCPs]
2. [Pitfall 2] - Source: [agents/MCPs]

### Recommended Approach
[Synthesized recommendation based on all findings]

## Evidence Quality Assessment

| Category | Sources | Confidence | Notes |
|----------|---------|------------|-------|
| Official Docs | N | High | [ref/context7] |
| Code Examples | N | High | [exa] |
| Community | N | Medium | [brave/search-specialist] |
| Trends | N | Medium | [trend-researcher] |
| UX Insights | N | Low-Medium | [ux-researcher] |

## Information Gaps

[List any unanswered questions or areas needing deeper research]

## Sources

### Official Documentation
- [Source 1](URL) - ref MCP
- [Source 2](URL) - context7 MCP

### Code Repositories & Examples
- [Source 3](URL) - exa MCP
- [Source 4](URL) - exa MCP

### Community Discussions
- [Source 5](URL) - brave-search MCP
- [Source 6](URL) - search-specialist agent

### Market Analysis
- [Source 7](URL) - trend-researcher agent
- [Source 8](URL) - exa MCP

### User Research
- [Source 9](URL) - ux-researcher agent
- [Source 10](URL) - brave-search MCP

## Research Metadata

**Agents Used**:
- deep-research-agent: [time spent]
- search-specialist: [time spent]
- trend-researcher: [time spent]
- ux-researcher: [time spent]

**MCPs Used**:
- ref MCP: [queries count] - [token savings]
- exa MCP: [queries count] - [avg latency]
- brave-search MCP: [queries count] - [result types]
- context7 MCP: [queries count] - [mode used]
- sequential-thinking MCP: [thoughts generated]

**Performance Metrics**:
- Total research time: [duration]
- Parallel efficiency: [% time saved vs sequential]
- Cache hit rate: [%]
- Average query latency: [seconds]
- Total tokens consumed: [count]
- Token efficiency gain: [% vs baseline]

Advanced Patterns

Multi-Stage Research Workflow

For comprehensive research requiring multiple phases:

Stage 1: Discovery (Parallel agents + broad MCPs)
├─ Identify information landscape
├─ Map available sources
└─ Define research boundaries

Stage 2: Deep Dive (Focused MCP usage)
├─ Documentation analysis (ref + context7)
├─ Code pattern extraction (exa)
├─ Community sentiment (brave + search-specialist)
└─ Trend analysis (trend-researcher)

Stage 3: Verification (Cross-reference)
├─ Validate claims across sources
├─ Resolve contradictions (sequential-thinking)
└─ Assess confidence levels

Stage 4: Synthesis (deep-research-agent)
├─ Identify patterns
├─ Extract best practices
├─ Generate actionable insights
└─ Document evidence chains

Adaptive Depth Strategy

Match research depth to query importance:

Quick (5 min): Single-hop, primary MCPs only

- 1 agent (search-specialist)
- 2 MCPs (ref OR exa + brave)
- Summary output

Standard (15 min): Multi-hop, 2-3 agents

- 2-3 agents (search-specialist + deep-research-agent + trend-researcher)
- 3 MCPs (ref + exa + brave)
- Structured report

Deep (45 min): Comprehensive, all agents

- 4 agents (all parallel)
- 4 MCPs (ref + exa + brave + context7)
- Full report with evidence chains

Exhaustive (2+ hrs): Maximum depth, multi-stage

- 4 agents (parallel + sequential refinement)
- 4 MCPs + sequential-thinking
- Multi-stage workflow
- Iterative validation
- Complete documentation

Error Handling & Fallbacks

Rate Limiting

If MCP rate limited:

1. Implement exponential backoff (1s, 2s, 4s)
2. Switch to alternative MCP (ref → exa → brave)
3. Use cached results if available
4. Queue requests for later execution

Quality Threshold Violations

If results below confidence threshold:

1. Invoke fallback MCP chain
2. Cross-verify with additional agent
3. Use sequential-thinking to analyze gaps
4. Explicitly note uncertainty in output

Agent Failure

If agent fails to complete:

1. Continue with remaining agents
2. Note reduced coverage in metadata
3. Suggest re-running failed agent separately
4. Provide partial results with caveats

Performance Monitoring

Track these metrics per research session:

Efficiency Metrics:
- Parallel execution time savings
- Cache hit rate
- Token consumption vs baseline
- Cost per query

Quality Metrics:
- Source diversity (unique domains)
- Citation completeness
- Confidence score distribution
- Information gap rate

MCP Performance:
- Latency by MCP (ref: 1.7s, exa: 1.18s, brave: <2s)
- Success rate by MCP (target: >95%)
- Token efficiency (ref: 96% reduction)
- Fallback invocation frequency

Integration with /sc:research

This skill automatically activates when /sc:research is invoked. The command passes:

• Query string
• Depth flag (--depth)
• Strategy flag (--strategy)
• MCP flags (--ref, --exa, --brave-search, --c7)
• Thinking flags (--seq, --ultrathink)

The skill then:

1. Parses flags to determine execution strategy
2. Launches appropriate agents in parallel
3. Orchestrates MCP tool usage per best practices
4. Generates research report in standard format
5. Saves to claudedocs/research_[topic]_[timestamp].md

Examples

Example 1: Technical Documentation Research

Query: "Next.js App Router best practices and common pitfalls"

Execution:
1. Parallel agents launch (4 simultaneous)
2. ref MCP: Official Next.js docs (token-efficient)
3. exa MCP: GitHub repos with App Router examples
4. brave-search MCP: Reddit/GitHub discussions
5. context7 MCP: Vercel Next.js documentation
6. sequential-thinking: Analyze contradictions in community advice
7. Synthesis: Best practices + pitfalls + recommended approach

Output: Comprehensive guide with official docs + real code + community insights

Example 2: Market Research

Query: "AI coding assistant market analysis 2024"

Execution:
1. Parallel agents:
   - search-specialist: Product websites, official announcements
   - trend-researcher: TikTok/Twitter trends, viral content
   - ux-researcher: User reviews, pain points
   - deep-research-agent: Competitive analysis, feature comparison
2. brave-search MCP: Latest news, funding announcements
3. exa MCP: Company research, technical capabilities
4. ref MCP: API documentation analysis
5. sequential-thinking: Cost-benefit analysis, market positioning

Output: Market landscape + trends + competitive matrix + opportunities

Example 3: Technical Problem Solving

Query: "How to implement real-time collaboration in React with TypeScript"

Execution:
1. Parallel agents launch
2. ref MCP: React + TypeScript official docs
3. exa MCP:
   - Code examples (WebSocket, CRDTs, Yjs)
   - GitHub repos with implementations
4. brave-search MCP: Tutorials, blog posts, case studies
5. context7 MCP: Library docs (Socket.io, Y.js, etc.)
6. sequential-thinking: Evaluate approaches (WebSocket vs SSE vs WebRTC)

Output: Implementation guide + code examples + tradeoff analysis

Success Criteria

Research is successful when:

• All 4 agents executed (or failures documented)
• At least 3 MCPs utilized per best practices
• Parallel execution achieved (single message, multiple Task calls)
• Output includes confidence scores and source citations
• Evidence chains are traceable
• Information gaps explicitly identified
• Report saved to claudedocs/ directory
• Performance metrics documented

Troubleshooting

Issue: Agents not executing in parallel

Solution: Ensure single message with multiple Task tool calls, not sequential messages

Issue: High token consumption

Solution:

• Use ref MCP for docs (96% reduction)
• Configure exa tokensNum appropriately
• Enable only needed exa tools
• Implement caching

Issue: Rate limiting errors

Solution:

• Implement request queuing
• Use fallback MCP chains
• Enable exponential backoff

Issue: Low quality results

Solution:

• Increase depth level
• Add more agents
• Use sequential-thinking for gap analysis
• Enable multi-stage verification

References

• MCP Best Practices: See MCP_BEST_PRACTICES_COMPREHENSIVE_GUIDE.md
• Skill Best Practices: See skill.best.practices.md
• Research Command: See .claude/commands/sc/research.md

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Skill Version: 1.0 Last Updated: 2025-11-24 Maintainer: Ultimate Research System License: Internal Use