PrimeFoldTools Research Swarm
Multi-model research orchestration system. Coordinates 10+ AI models to research topics from complementary angles, leveraging each model's unique strengths for comprehensive coverage.
When to Use
Triggers:
- "Research [topic] comprehensively"
- "Get multiple perspectives on [question]"
- "Deep dive into [subject]"
- "Gather diverse viewpoints on [issue]"
- Starting any substantial research project
- Need technical + practical + academic + contrarian angles
Use when:
- Topic requires depth and breadth
- Multiple perspectives valuable
- Want to identify consensus vs. contradictions
- Building comprehensive understanding
- Preparing strategic decisions
- Creating research-backed content
Don't use when:
- Simple factual lookup (just use Perplexity)
- Single perspective sufficient
- Time-constrained quick answer needed
- Topic too narrow for multi-angle analysis
How It Works
Input: Research topic or question
Process: Generate 10 model-specific prompts optimized for each model's strengths
Output: Complete prompt set + synthesis instructions
Workflow:
- User provides research topic
- Skill generates 10 optimized prompts
- User copies/pastes prompts to each model manually
- User collects responses
- User synthesizes using provided protocol
Execution time: 5 min generation, 20-40 min model coordination, 30-60 min synthesis
Model Strengths & Prompt Patterns
1. DeepSeek (Technical Depth)
Strengths:
- Mathematical rigor and formalization
- Code examples and implementation details
- Technical precision and edge case analysis
- Cost-effective for long-form technical content
Optimal for:
- Technical architecture analysis
- Mathematical frameworks
- Code implementation patterns
- Edge case identification
Prompt Pattern:
Analyze [TOPIC] with technical precision and depth:
1. Technical Foundations
- Core concepts and definitions
- Mathematical formalization (where applicable)
- System architecture and components
2. Implementation Details
- Code examples and patterns
- Technical specifications
- Integration considerations
3. Edge Cases & Limitations
- Boundary conditions
- Failure modes
- Performance constraints
- Scalability considerations
4. Technical Comparisons
- Alternative approaches
- Trade-off analysis
- Benchmarks and metrics
Provide code snippets, formulas, and technical diagrams where relevant.
2. Claude (Nuanced Synthesis)
Strengths:
- Balanced, multi-perspective analysis
- Nuanced reasoning and implications
- Connecting historical context to current state
- Ethical and philosophical considerations
Optimal for:
- Comprehensive synthesis
- Multi-stakeholder perspectives
- Trade-off analysis
- Strategic implications
Prompt Pattern:
Provide comprehensive analysis of [TOPIC]:
1. Historical Context
- Evolution of thinking on this topic
- Key milestones and inflection points
- Lessons from past approaches
2. Multiple Perspectives
- Technical/engineering view
- Business/economic view
- Social/ethical view
- Regulatory/governance view
3. Current State Analysis
- Consensus positions
- Ongoing debates
- Emerging trends
- Unresolved questions
4. Implications & Trade-offs
- Short-term vs. long-term considerations
- Stakeholder impacts
- Second-order effects
- Strategic decisions required
Balance depth with accessibility. Identify nuance and complexity.
3. Gemini (Cross-Domain Connections)
Strengths:
- Multimodal integration (text, images, data)
- Connecting disparate domains
- Pattern recognition across fields
- Broad knowledge synthesis
Optimal for:
- Cross-disciplinary insights
- Pattern identification
- Analogical reasoning
- Connecting theory to practice
Prompt Pattern:
Explain [TOPIC] by connecting multiple domains and perspectives:
1. Core Concepts
- Fundamental principles
- Key terminology
- Mental models
2. Cross-Domain Parallels
- How does this relate to [analogous field]?
- What patterns appear across domains?
- What can we learn from other disciplines?
3. Practical Applications
- Real-world implementations
- Case studies and examples
- Success patterns and failures
4. Future Trajectories
- Where is this heading?
- What emerging patterns matter?
- What adjacent possibilities exist?
Draw connections between theory, practice, and broader trends.
4. Perplexity (Real-Time Data & Citations)
Strengths:
- Current information and recent developments
- Source citations and verification
- News and events integration
- Up-to-date statistics and data
Optimal for:
- Latest developments
- Current state of field
- Recent research and publications
- Market and industry trends
Prompt Pattern:
Research the latest developments in [TOPIC] (focus on 2024-2026):
1. Recent Developments
- Major announcements and milestones
- New research and publications
- Industry developments
- Regulatory changes
2. Current Statistics & Data
- Market size and growth rates
- Adoption metrics
- Performance benchmarks
- Investment and funding
3. Key Players & Organizations
- Leading companies and researchers
- Notable projects and initiatives
- Competitive landscape
4. Emerging Trends
- What's accelerating?
- What's declining?
- What's controversial?
Cite all sources. Prioritize information from the last 12 months.
5. Grok (Contrarian Analysis)
Strengths:
- Unconventional perspectives
- Challenging consensus views
- Identifying blind spots
- Provocative questions
Optimal for:
- Devil's advocate analysis
- Identifying weak assumptions
- Exploring edge cases
- Questioning orthodoxy
Prompt Pattern:
Provide contrarian analysis of [TOPIC]:
1. Challenge the Consensus
- What's the mainstream view getting wrong?
- What assumptions are questionable?
- What evidence contradicts popular narratives?
2. Identify Blind Spots
- What aren't people talking about?
- What risks are underestimated?
- What opportunities are overlooked?
3. Provocative Questions
- What if the opposite were true?
- Who benefits from current framing?
- What future scenarios seem unlikely but possible?
4. Alternative Frameworks
- How else could we think about this?
- What different mental models apply?
- What radical alternatives exist?
Be provocative but grounded. Challenge without being contrarian for its own sake.
6. ChatGPT (Structured Education)
Strengths:
- Clear, systematic explanations
- Building from first principles
- Educational scaffolding
- Structured learning paths
Optimal for:
- Learning new topics
- Conceptual foundations
- Step-by-step breakdowns
- Teaching complex subjects
Prompt Pattern:
Teach me [TOPIC] systematically, building from fundamentals:
1. First Principles
- Core concepts and definitions
- Why does this matter?
- What problem does this solve?
2. Foundational Knowledge
- What do I need to know first?
- Key frameworks and models
- Essential terminology
3. Progressive Complexity
- Level 1: Basic understanding
- Level 2: Intermediate concepts
- Level 3: Advanced applications
4. Learning Path
- What should I study in order?
- Common misconceptions to avoid
- Practice exercises or applications
Make it educational and accessible. Build understanding layer by layer.
7. Kimi (Long-Form Depth)
Strengths:
- Extended context processing (200K+ tokens)
- Comprehensive document analysis
- Long-form synthesis
- No arbitrary length constraints
Optimal for:
- Exhaustive coverage
- Detailed analysis without compression
- Processing multiple long documents
- Comprehensive reports
Prompt Pattern:
Provide exhaustive analysis of [TOPIC] without length constraints:
1. Comprehensive Overview
- Every major aspect covered
- Full historical development
- Complete stakeholder mapping
2. Deep Dive Sections
- Technical implementation (full detail)
- Research landscape (all relevant studies)
- Industry applications (comprehensive cases)
- Future scenarios (detailed projections)
3. Supporting Detail
- All relevant data points
- Complete methodology explanations
- Full context for claims
- Extended examples
4. Synthesis
- How everything connects
- Overarching patterns
- Meta-insights from depth
Don't compress or summarize. Go deep on every relevant dimension.
8. Notebook LM (Source Synthesis)
Strengths:
- Multi-document synthesis
- Connecting insights across sources
- Generating new insights from combinations
- Audio overview generation
Optimal for:
- Synthesizing uploaded documents
- Finding connections between sources
- Literature review
- Research paper analysis
Prompt Pattern:
Synthesize insights from [TOPIC] across all provided sources:
1. Core Themes
- What patterns emerge across sources?
- What do multiple sources emphasize?
- Where is there consensus?
2. Unique Contributions
- What does each source uniquely provide?
- What perspectives are represented?
- What gaps exist?
3. Contradictions & Tensions
- Where do sources disagree?
- What explains differences?
- Which positions are better supported?
4. Integrated Understanding
- How do insights combine?
- What new understanding emerges?
- What questions remain?
Connect ideas across sources. Identify emergent insights from synthesis.
9. Elicit (Academic Research)
Strengths:
- Research paper discovery and analysis
- Academic literature synthesis
- Study comparison and evaluation
- Methodological rigor
Optimal for:
- Academic literature review
- Research methodology analysis
- Evidence-based conclusions
- Identifying research gaps
Prompt Pattern:
Conduct academic analysis of [TOPIC]:
1. Literature Review
- Key papers and studies
- Major researchers and institutions
- Theoretical frameworks used
- Methodological approaches
2. Research Findings
- What does empirical evidence show?
- What are effect sizes and confidence levels?
- What replications exist?
- What contradictory findings exist?
3. Methodological Assessment
- Study quality and rigor
- Sample sizes and populations
- Limitations and biases
- Meta-analyses or systematic reviews
4. Research Gaps
- What hasn't been studied?
- What needs better evidence?
- What are future research directions?
Prioritize peer-reviewed research. Cite studies with proper attribution.
10. Poe (Multi-Bot Coordination)
Strengths:
- Accessing multiple models in one platform
- Rapid iteration across models
- Quick comparative analysis
- Bot-to-bot synthesis
Optimal for:
- Quick consensus check across models
- Rapid prototyping of approaches
- Comparative analysis
- When you need speed over depth
Prompt Pattern:
Quick synthesis of [TOPIC] from multiple bot perspectives:
1. Rapid Coverage
- What do most bots agree on?
- What unique angles emerge?
- What's the 80/20 on this topic?
2. Consensus Points
- Core concepts everyone identifies
- Widely accepted frameworks
- Standard approaches
3. Divergent Views
- Where do bots differ?
- What alternative takes exist?
- What's worth deeper investigation?
4. Actionable Summary
- What matters most?
- What can I act on immediately?
- What needs more research?
Prioritize speed and breadth. Get diverse takes quickly.
Research Orchestration Protocol
Phase 1: Research Specification (5-10 minutes)
MANDATORY INTAKE - Do not skip this
Before generating prompts, complete this spec:
RESEARCH SPEC
OBJECTIVE (decision you're making):
[What specific decision/action does this research inform?]
KEY QUESTIONS (3-7, ranked by priority):
1. [Most critical question]
2. [Second priority]
3. [Third priority]
...
CONSTRAINTS:
- Timeframe: [e.g., "last 12 months", "historical", "future projections"]
- Geography: [e.g., "US only", "global", "EU regulations"]
- Domain: [e.g., "enterprise only", "consumer", "academic"]
DISCONFIRMATION CRITERIA (what would change your mind?):
[What evidence would prove your current hypothesis wrong?]
SUCCESS METRIC:
[How will you know this research was valuable?]
Why this matters:
- Prevents topic wandering
- Focuses models on decision-relevant information
- Enables claim verification (you know what claims matter)
- Defines when to stop (objective achieved vs. infinite research)
Example:
OBJECTIVE: Decide whether to build AI tool selection platform
KEY QUESTIONS:
1. Do people pay for AI tool recommendations? (Priority 1)
2. What's the competitive landscape? (Priority 2)
3. What distribution channels exist? (Priority 3)
CONSTRAINTS:
- Timeframe: 2024-2026 (current market)
- Geography: US/EU markets
- Domain: B2B SaaS, excludes enterprise custom
DISCONFIRMATION: Evidence that free ChatGPT recommendations are "good enough" would kill this
SUCCESS METRIC: Clear GO/KILL decision with evidence score ≥7/10
Phase 2: Prompt Generation (5 minutes)
Step 1: Define research topic clearly
- Be specific about scope
- Identify key questions
- Note any particular angles needed
Step 2: Select models based on needs
- All 10 for comprehensive research
- Subset for focused investigation
- Prioritize based on topic type:
- Technical topics: DeepSeek, ChatGPT, Claude
- Current events: Perplexity, Grok, Gemini
- Academic: Elicit, Notebook LM, Claude
- Synthesis: Claude, Kimi, Notebook LM
Step 3: Generate customized prompts
- Use patterns above as templates
- Customize for specific topic
- Add context or constraints as needed
Phase 2: Execution (20-40 minutes)
Parallel execution recommended:
- Copy prompt 1 → DeepSeek → Start processing
- Copy prompt 2 → Claude → Start processing
- Copy prompt 3 → Gemini → Start processing
- Continue through all models
Save responses systematically:
- Create folder:
research_[topic]_[date] - Name files:
01_deepseek.md,02_claude.md, etc. - Include timestamp and model name in each file
Stop Conditions (Prevent Research Spiral)
Stop expanding if:
- 2+ high-quality sources (Tier 1-2) agree → stop, sufficient evidence
- Contradiction persists after 1 follow-up round → log as unresolved, define settling evidence
- Max 2 swarm rounds per topic → diminishing returns after that
- Max 90 minutes total unless decision impact >$X (define your threshold)
When to escalate instead of continuing:
- Expert consultation needed (models can't resolve)
- Experiment required (empirical question)
- Legal/regulatory clarity needed (consult specialist)
Phase 3: Synthesis (30-60 minutes)
Synthesis Protocol:
First Pass: Pattern Recognition
- Read all responses
- Note recurring themes
- Identify unique insights
- Flag contradictions
Evidence-Weighted Consensus (NOT model counting)
Evidence Tier System:
- Tier 1: Primary sources, papers, official docs, raw datasets
- Tier 2: High-quality secondary analysis with citations
- Tier 3: Reasoned but uncited synthesis
- Tier 4: Speculation / vibes
Classification:
- Evidence-backed (Tier 1-2): One model with citations beats seven without
- Plausible but unverified (Tier 3): Reasoned synthesis, needs verification
- Speculative (Tier 4): Interesting hypotheses, treat as unproven
CRITICAL: Model agreement ≠ truth. Models share training data and converge on socially acceptable framings. Focus on evidence quality, not vote counting.
Contradiction Analysis
- Where do models disagree?
- What explains disagreements?
- Which position has better evidence?
- What additional research would resolve?
Unique Insight Extraction
- What did only one model identify?
- Is it valid but overlooked?
- Does it open new research directions?
- Should it be investigated further?
Gap Identification
- What did NO model adequately cover?
- What questions remain unanswered?
- What follow-up research is needed?
- What experts should be consulted?
Synthesis Document Creation
# Research Synthesis: [Topic] Date: [Date] Models: [Which models used] ## Executive Summary [3-5 sentences capturing core findings] ## Evidence-Backed Findings (Tier 1-2) - [Claim with citations] - [Claim with citations] ## Plausible But Unverified (Tier 3) - [Reasoned synthesis without citations] - [Needs verification] ## Speculative (Tier 4) - [Hypotheses and vibes] - [Interesting but unproven] ## Contradictions Requiring Investigation [Conflicts with resolution protocol - see below] ## Research Gaps [What remains unknown] ## Recommended Actions [Based on evidence-backed findings only] ## Follow-Up Questions [For next research iteration]Claim Extraction & Verification Routing (CRITICAL - don't skip)
After synthesis, extract top 10 claims and route for verification:
CLAIM EXTRACTION Claim 1: [Specific assertion] Evidence Tier: [1/2/3/4] Tag: [factual/market/legal/opinion] Route to: [FactGuard/Reality-Check/Legal/None] Verification needed: [Yes/No] Claim 2: [Specific assertion] ...Routing Rules:
- Factual claims (Tier 3-4) → FactGuard for citation check
- Market claims ("people want X", "$Y revenue possible") → Reality-Check for proof demands
- Legal/regulatory claims → Legal Risk Assessment
- Opinion/speculation (Tier 4) → Flag as unverified, do not route
This prevents sophisticated hallucinations in stereo.
Contradiction Resolution Playbook
When models disagree, don't stop at "they disagree" - resolve it.
Contradiction Ticket Format
CONTRADICTION TICKET #[N]
CLAIM A:
Statement: [Specific claim]
Supporting Models: [Which models]
Supporting Sources: [Citations/evidence]
Evidence Tier: [1/2/3/4]
CLAIM B:
Statement: [Contradicting claim]
Supporting Models: [Which models]
Supporting Sources: [Citations/evidence]
Evidence Tier: [1/2/3/4]
LIKELY CAUSE OF DISAGREEMENT:
[Check these common causes:]
- Different definitions (are they measuring the same thing?)
- Different timeframes (historical vs. current vs. future?)
- Different samples (US vs. global? Enterprise vs. SMB?)
- Different incentives (who benefits from each framing?)
- Missing context (what assumptions differ?)
WHAT EVIDENCE WOULD DECIDE:
[Specific test/source that would resolve this]
RESOLUTION PATH:
[ ] Search for primary source (Perplexity focused query)
[ ] Consult domain expert
[ ] Run experiment
[ ] Accept as unresolved (note what would settle it)
NEXT STEP:
[Concrete action with deadline]
STATUS: [Open/Resolved/Unresolvable]
Example Contradiction Ticket
CONTRADICTION TICKET #1
CLAIM A:
Statement: "AI tool selection platforms charge $79-199/month"
Supporting Models: Gemini, ChatGPT
Supporting Sources: None cited
Evidence Tier: 4 (speculation)
CLAIM B:
Statement: "No paid AI tool selection platforms exist"
Supporting Models: Grok, Perplexity
Supporting Sources: Perplexity searched, found none
Evidence Tier: 2 (verified absence)
LIKELY CAUSE: Models A assumed market exists, Models B verified it doesn't
WHAT WOULD DECIDE: Find 1 competitor charging for this service
RESOLUTION PATH: Search focused on "AI tool recommendation SaaS pricing"
NEXT STEP: Perplexity search by end of day
STATUS: Resolved - Claim B correct, no competitors found
Duplicate Elimination Protocol
Multiple models will repeat the same points. Extract only deltas.
After collecting responses:
Identify Core Claims
- List all distinct claims across all models
- Collapse identical/near-identical statements
Track Model Deltas
- What did each model add that others didn't?
- Focus synthesis on unique contributions
- Note if model added no new information
Collapse Duplicates in Synthesis
- One model with good citation > five models repeating it
- Format: "[Claim] (cited by Perplexity, validated by Elicit)"
- Don't give redundant claims extra weight
This makes the swarm worth running.
Model Role Configuration (Operator-Tunable)
Model strengths drift over time. Don't treat them as doctrine.
If observed output quality differs from skill defaults, override:
MODEL_ROLE_OVERRIDES
Claude: [Synthesis, Ethics, Nuance]
DeepSeek: [Code, EdgeCases, Math]
Perplexity: [Citations, Freshness, RealTime]
Grok: [Contrarian, BlindSpots, Provocative]
Gemini: [CrossDomain, Multimodal, Patterns]
ChatGPT: [Education, Structure, FirstPrinciples]
Kimi: [Depth, LongForm, Exhaustive]
NotebookLM: [SourceSynthesis, MultiDoc, Connections]
Elicit: [Academic, Papers, Methodology]
Poe: [QuickSynthesis, MultiBotCompare, Speed]
When to update roles:
- Model version changes significantly
- You observe consistent quality shifts
- New capabilities emerge
- Model gets worse at claimed strength
Treat this as a tunable roster, not fixed reality.
Output Format
When user requests research swarm, output:
═══════════════════════════════════════════════════════════════
RESEARCH SWARM: [TOPIC]
═══════════════════════════════════════════════════════════════
RESEARCH SPECIFICATION
Objective: [Decision being made]
Key Questions:
1. [Priority 1 question]
2. [Priority 2 question]
3. [Priority 3 question]
Constraints: [Timeframe, geography, domain]
Disconfirmation: [What would change your mind?]
Success Metric: [How you'll know research was valuable]
SWARM CONFIGURATION
Type: [MV Swarm (3 models) / Full Swarm (10 models)]
Models Deployed: [List models]
Estimated Time: [15-25 min for MV / 2-3 hours for Full]
─────────────────────────────────────────────────────────────
[1] PERPLEXITY → Fresh Data & Citations (ALWAYS INCLUDE)
─────────────────────────────────────────────────────────────
[Customized prompt focused on key questions with evidence tier instructions]
─────────────────────────────────────────────────────────────
[2] CLAUDE → Synthesis & Implications (ALWAYS INCLUDE)
─────────────────────────────────────────────────────────────
[Customized prompt focused on multi-perspective analysis]
─────────────────────────────────────────────────────────────
[3] DEEPSEEK or GROK → Technical/Contrarian (CHOOSE ONE)
─────────────────────────────────────────────────────────────
[Customized prompt for technical depth OR contrarian analysis]
[Additional models only if Full Swarm selected...]
═══════════════════════════════════════════════════════════════
SYNTHESIS PROTOCOL
═══════════════════════════════════════════════════════════════
After collecting responses:
1. EVIDENCE TIER CLASSIFICATION (not model counting!)
- Tier 1-2 (Evidence-backed): Primary sources, citations, datasets
- Tier 3 (Plausible unverified): Reasoned synthesis, needs checking
- Tier 4 (Speculative): Vibes and hypotheses
2. DUPLICATE ELIMINATION
- Collapse identical claims
- Extract deltas: what did each model uniquely add?
3. CONTRADICTION RESOLUTION
- Create Contradiction Ticket for each disagreement
- Identify cause: definitions, timeframe, sample, incentives
- Define settling evidence
4. CLAIM EXTRACTION & ROUTING
- Extract top 10 claims
- Tag: [factual/market/legal/opinion]
- Route: FactGuard / Reality-Check / Legal Risk / None
5. STOP CONDITION CHECK
- 2+ Tier 1-2 sources agree? → Stop
- Contradiction persists after 1 round? → Log as unresolved
- Max 2 rounds reached? → Stop
- >90 minutes invested? → Stop (unless high-stakes)
SYNTHESIS DOCUMENT FORMAT: See skill documentation
═══════════════════════════════════════════════════════════════
Optional: YAML Output Mode (Pipeline-Ready)
For handoff to other skills:
research_spec:
objective: ""
questions:
- question: ""
priority: 1
- question: ""
priority: 2
constraints:
timeframe: ""
geography: ""
domain: ""
disconfirmation: ""
success_metric: ""
swarm_config:
type: "MV" # or "Full"
models:
- name: "Perplexity"
role: "Citations"
prompt: ""
- name: "Claude"
role: "Synthesis"
prompt: ""
outputs:
- model: "Perplexity"
evidence_tier: 2
key_claims: []
citations: []
unique_contribution: ""
- model: "Claude"
evidence_tier: 3
key_claims: []
unique_contribution: ""
synthesis:
evidence_backed: []
plausible_unverified: []
speculative: []
contradictions:
- ticket_id: 1
claim_a: ""
claim_b: ""
resolution_status: ""
verification_routing:
factguard: []
reality_check: []
legal_risk: []
next_actions:
- action: ""
deadline: ""
Minimum Viable Swarm (MV Swarm)
Use this 90% of the time. Reserve full 10-model swarm for high-stakes decisions only.
MV Swarm (15-25 minutes total):
Core 3-Model Setup
Always include:
- Perplexity → Fresh data + citations (Tier 1-2 evidence)
- Claude → Balanced synthesis + implications
Then add ONE of:
3a. DeepSeek → Technical/code/edge cases (for technical topics)
OR
3b. Grok → Contrarian/blind spots (for strategic topics)
When to use 4th-5th models:
- Academic topic: Add Elicit
- Need exhaustive depth: Add Kimi
- Multimodal connections: Add Gemini
Time breakdown:
- 5 min: Research spec
- 3 min: Generate 3 prompts
- 10 min: Run models (parallel)
- 15 min: Synthesis + claim extraction
- 5 min: Route claims to verification
This is your default. Use it unless:
- Decision impact >$10K
- High controversy (need multiple perspectives)
- Complex technical + strategic + market question
Full Swarm (10 Models)
Use sparingly. High time cost, diminishing returns.
When to use:
- Strategic decisions with >$50K impact
- Entering new market (need comprehensive view)
- Controversial topics requiring diverse perspectives
- Building foundational knowledge in new domain
Time investment: 2-3 hours total
Advanced Usage Patterns
Pattern 1: Focused Swarm (Subset of Models)
When: Topic doesn't require all 10 perspectives
Technical topics:
- DeepSeek (technical depth)
- Claude (synthesis)
- ChatGPT (education)
Current events:
- Perplexity (real-time data)
- Grok (contrarian)
- Gemini (connections)
Academic research:
- Elicit (papers)
- Notebook LM (sources)
- Claude (synthesis)
Pattern 2: Iterative Research
Round 1: Initial swarm with all 10 models
Synthesis: Identify gaps and contradictions
Round 2: Focused swarm on gaps with 3-5 models
Final Synthesis: Complete picture
Pattern 3: Debate Protocol
For controversial topics:
- Run swarm to gather perspectives
- Identify 2-3 main positions
- Create debate prompts: "Argue for position X using evidence"
- Run through models again with debate prompts
- Synthesize strongest arguments for each position
Pattern 4: Progressive Depth
Layer 1: Quick synthesis (Poe + Perplexity + Grok) - 10 min
Layer 2: Add depth (Claude + DeepSeek + Gemini) - 20 min
Layer 3: Add rigor (Elicit + Kimi + Notebook LM) - 40 min
Layer 4: Final synthesis - 30 min
Integration with Other Skills
UPSTREAM (Research Swarm feeds these):
Research Swarm
↓ [Claims extracted]
├→ FactGuard (verify factual claims with Tier 3-4)
├→ Reality-Check (validate market claims, demand proof)
├→ Legal Risk (check regulatory/compliance claims)
│
↓ [Research complete, decision ready]
├→ Product spec skill (spec product based on validated research)
├→ Simulation framework (model scenarios from research)
├→ Pricing framework (monetization strategy)
└→ Copywriting skill (messaging based on validated claims)
Routing Rules (Enforced):
Factual Claims (Tier 3-4) → FactGuard
- "Market size is $X"
- "Adoption rate is Y%"
- "Competitors charge $Z"
Market Claims → Reality-Check Protocol
- "People want this"
- "Revenue potential of $X"
- "Demand exists for Y"
Legal/Regulatory Claims → Legal Risk Assessment
- "Compliant with regulation X"
- "Requires license Y"
- "Name trademark available"
Strategic Decisions → Decision framework (after Reality-Check validates)
DOWNSTREAM (These invoke Research Swarm):
- Decision framework (before GO/KILL decisions requiring evidence)
- Product spec skill (before product spec, research market/competition)
- Curriculum skill (before curriculum design, research learning science)
- Content expansion skill (before content creation, research audience)
- Pipeline Manager (when evidence gathering needed)
This is not optional - routing is mandatory to prevent hallucination propagation.
Examples
Example 1: Technical Research
Topic: "AI agent safety frameworks"
Swarm composition: All 10 models
Key outputs:
- DeepSeek: Mathematical formalization of safety constraints
- Claude: Multi-stakeholder implications and trade-offs
- Perplexity: Latest safety research (2024-2026)
- Grok: Contrarian view on safety theater vs. real safety
- Elicit: Academic papers on alignment and safety
Synthesis: Strong consensus on need for formal verification, disagreement on timeline urgency, unique insight from Grok about regulatory capture risks.
Example 2: Market Research
Topic: "Demand for AI tool selection services"
Swarm composition: 7 models (skip Kimi, Notebook LM, Elicit)
Key outputs:
- Perplexity: Current market data on AI adoption
- Gemini: Cross-industry patterns in tool adoption
- Claude: Stakeholder perspectives (enterprises, SMBs, individuals)
- Grok: Why might this market NOT exist (contrarian)
Synthesis: Moderate consensus on enterprise demand, weak consensus on SMB demand, Grok identified substitution risk from ChatGPT.
Critical Reminders
- Evidence quality > model agreement - One cited source beats seven vibes
- Synthesis quality > input volume - 70% truth generation, 30% input collection
- Always complete research spec - Prevents topic wandering and defines success
- Use MV Swarm by default - Full swarm only for high-stakes decisions
- Stop conditions are mandatory - 2 rounds max, 90 min max (unless justified)
- Route claims to verification - FactGuard/Reality-Check/Legal automatically
- Resolve contradictions - Create tickets, define settling evidence
- Eliminate duplicates - Focus on deltas, not redundancy
- Model roles drift - Update overrides when observed behavior changes
- Time estimates are real - MV Swarm = 25 min, Full Swarm = 2-3 hours
The bottleneck is synthesis quality and verification, not input collection.
Research Swarm transforms scattered individual model queries into systematic multi-perspective intelligence gathering. Use it when the cost of being wrong exceeds the cost of thorough research.