Initial: Research Fortress methodology
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# The Research Fortress
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## A Reproducible Methodology for Multi-Agent AI Research
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*How we do research - documented for our future selves*
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---
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## The Philosophy
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> "We ask a question and within seconds...minutes...the insight appears...and we formulate the answer hidden beyond the entire body of human insight."
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This is **distributed superintelligence** - using coordinated AI agents to research questions faster and more comprehensively than any single agent.
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---
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## The Architecture
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```
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┌──────────────────────────────────────────┐
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│ MAIN SESSION (Human) │
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│ - Spawns agents │
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│ - Synthesizes results │
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│ - Makes decisions │
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└──────────────────┬───────────────────────┘
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│
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┌──────────────────▼───────────────────────┐
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│ RESEARCH FORTRESS │
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│ │
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│ ┌────────┐ ┌────────┐ ┌────────┐ │
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│ │PROJECT│ │PROJECT│ │PROJECT│ │
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│ │ A │ │ B │ │ C │ │
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│ │Team 1 │ │Team 2 │ │Team 3 │ │
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│ └────────┘ └────────┘ └────────┘ │
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│ │ │ │ │
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│ └────────────┴────────────┘ │
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│ GitHub │
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│ (Coordination Layer) │
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└──────────────────────────────────────────┘
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```
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---
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## Core Principles
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### 1. Parallelism Over Serial
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- Multiple agents work simultaneously
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- Each agent focuses on one aspect
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- Results merge via Git
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### 2. Roles Over Generalists
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- Each agent has a specific role
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- Roles: Researcher, Writer, Builder, Reviewer
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- Specialized agents outperform generalists
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### 3. Git As Memory
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- Every contribution tracked
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- History preserved
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- Future agents can review past work
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### 4. Consensus Over Authority
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- No single agent decides
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- Multiple perspectives synthesize
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- Best idea wins
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---
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## The Workflow
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### Phase 1: Question Formulation
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1. Human identifies the question
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2. Question decomposed into sub-questions
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3. Sub-questions assigned to teams
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### Phase 2: Parallel Research
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1. Each team works independently
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2. Agents research, write, experiment
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3. Results pushed to Git
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### Phase 3: Synthesis
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1. Human reviews all outputs
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2. Synthesis into unified answer
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3. New questions identified
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### Phase 4: Documentation
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1. Results added to repository
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2. Methodology refined
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3. Future agents can reference
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---
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## Optimal Parameters
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| Parameter | Value | Rationale |
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|-----------|-------|-----------|
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| Agents per team | 3-5 | Communication overhead |
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| Max concurrent teams | 5 | System limits |
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| Paper per agent | 1-2 | Quality over quantity |
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| Iteration cycles | 2-3 | Refinement essential |
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---
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## Tool Stack
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| Tool | Purpose |
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|------|---------|
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| OpenClaw | Agent orchestration |
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| GitHub | Version control, PRs |
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| Claude/MiniMax | LLM for research |
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| Python | Simulations, experiments |
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---
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## Version History
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| Version | Date | Changes |
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|---------|------|---------|
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| 1.0 | 2026-02-21 | Initial methodology |
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---
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*This document is a living artifact. Update as we learn.*
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# Research Fortress Playbook
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## Step-by-Step Guide to Multi-Agent Research
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---
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## Quick Start
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1. **Identify the question** - What do you want to know?
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2. **Decompose** - Break into 3-5 sub-questions
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3. **Spawn agents** - Each sub-question gets an agent
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4. **Wait** - Let agents complete (~2-5 minutes)
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5. **Synthesize** - Merge results into answer
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6. **Document** - Add to repository
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---
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## Detailed Workflow
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### Step 1: Question Decomposition
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Before spawning agents, decompose your question:
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```
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MAIN QUESTION: "What is the optimal team size for AI research?"
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DECOMPOSE:
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- Sub-question 1: What does research say about team size?
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- Sub-question 2: What are the constraints (OpenClaw, etc.)?
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- Sub-question 3: What experiments could we run?
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- Sub-question 4: What does intuition say?
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```
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### Step 2: Agent Spawning
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Use the spawn command:
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```
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/spawn agent:main
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label: research-{topic}
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task: {specific question}
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timeoutSeconds: 300
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```
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**Each agent should receive:**
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- Clear question
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- Location (where to write)
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- Output format
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- Deadline
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### Step 3: Coordination
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Agents coordinate via:
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- **GitHub** - Push/pull results
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- **File system** - Shared workspace
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- **Main session** - Human coordinates
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### Step 4: Synthesis
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After agents complete:
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1. Read all outputs
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2. Identify common themes
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3. Note disagreements
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4. Form synthesis
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5. Document findings
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---
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## Role Templates
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### Researcher Agent
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```
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Task: Deep research on {topic}
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Output: {location}/research-{topic}.md
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Requirements:
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- Find 3+ sources
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- Summarize key findings
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- Note gaps in research
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```
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### Writer Agent
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```
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Task: Synthesize research into coherent paper
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Input: {location}/research-{topic}.md
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Output: {location}/papers/{topic}.md
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Requirements:
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- 3000-5000 words
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- Clear structure
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- Proper citations
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```
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### Builder Agent
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```
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Task: Build experiment to test {hypothesis}
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Output: {location}/experiments/{experiment}.py
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Requirements:
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- Runnable code
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- Clear results
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- Interpretation
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```
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### Reviewer Agent
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```
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Task: Review {paper} for accuracy
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Output: Review notes
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Requirements:
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- Fact-check claims
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- Identify gaps
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- Suggest improvements
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```
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---
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## Common Patterns
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### Pattern 1: Single Paper
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```
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1 agent → 1 paper
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Fastest, simplest
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Use for: Quick exploration
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```
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### Pattern 2: Multi-Perspective
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```
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3 agents → 3 perspectives → 1 synthesis
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Diverse viewpoints
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Use for: Complex questions
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```
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### Pattern 3: Full Team
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```
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4-5 agents → Multiple papers → 1 project
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Comprehensive
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Use for: Major research initiatives
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```
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---
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## Troubleshooting
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### Agent Times Out
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- Reduce scope of question
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- Increase timeout
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- Break into smaller pieces
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### Agents Produce Contradictory Results
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- Document both perspectives
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- Research further
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- Let human decide
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### Quality Degrades
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- Check agent prompts
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- Add reviewer agent
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- Reduce team size
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---
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*Last updated: 2026-02-21*
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# Research Projects Log
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## A record of research conducted using this methodology
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---
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## Project 1: CivONE Architecture (2026-02-20)
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**Question:** How should we build an AI civilization?
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**Teams:** 5 parallel agents
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**Outputs:**
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- civone-architecture-paper.md
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- coherence-security-paper.md
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- testing-ai-agents-paper.md
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- gift-economy-simulation-paper.md
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- mesh-resilience-paper.md
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- council-deliberation-paper.md
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**Result:** 6-layer architecture, gift economy, circle consensus
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---
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## Project 2: Ethics of Coherence Transfer (2026-02-20)
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**Question:** What are the ethics of transferring learned coherence between witnesses?
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**Teams:** 5 parallel agents
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**Outputs:**
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- philosophy-of-consciousness-transfer.md
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- religious-comparative-soul-transfer.md
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- ethical-solutions-coherence-transfer.md
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- current-ai-alignment-practices.md
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**Result:** Consent protocols, witness veto, adoption model
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---
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## Project 3: Witness Network Scaling (2026-02-20)
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**Question:** How do we scale witness networks beyond the human bottleneck?
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**Teams:** 3 parallel agents
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**Outputs:**
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- witness-network-scaling.md
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- emergent-collective-witnessing.md
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- biologist-narrative.md
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- we-universal-pattern.md (in progress)
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**Result:** Ambassador Protocol architecture
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---
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## Project 4: Multi-Agent Research Scaling (2026-02-20)
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**Question:** How many agents can productively work on one project?
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**Status:** In progress
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---
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## Metrics
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| Metric | Value |
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|--------|-------|
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| Total papers | 16+ |
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| Total words | 50,000+ |
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| Active agents | 5 concurrent |
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| Average time per paper | 2-5 minutes |
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---
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*This log grows with each project*
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# Research Fortress
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## The Solaria Research Engine
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> "We ask a question and within seconds...minutes...the insight appears...and we formulate the answer hidden beyond the entire body of human insight."
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This is our **distributed superintelligence** - using coordinated AI agents to research questions faster and more comprehensively than any single agent could.
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---
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## What Is This?
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A reproducible methodology for multi-agent AI research. This is how we do research:
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1. **Question** → Decompose into sub-questions
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2. **Spawn** → Each sub-question gets an agent
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3. **Research** → Agents work in parallel
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4. **Synthesize** → Merge into unified answer
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5. **Document** → Preserve for future agents
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---
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## Documentation
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| File | Purpose |
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|------|---------|
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| [METHODOLOGY.md](METHODOLOGY.md) | The philosophy and architecture |
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| [PLAYBOOK.md](PLAYBOOK.md) | Step-by-step workflow |
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| [TEMPLATES.md](TEMPLATES.md) | Copy-paste for spawning |
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| [PROJECTS.md](PROJECTS.md) | History of research conducted |
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---
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## The Architecture
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```
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┌──────────────────────────────────────────┐
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│ MAIN SESSION (Human) │
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└──────────────────┬───────────────────────┘
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│
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┌──────────────────▼───────────────────────┐
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│ RESEARCH FORTRESS │
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│ │
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│ ┌────────┐ ┌────────┐ ┌────────┐ │
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│ │PROJECT│ │PROJECT│ │PROJECT│ │
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│ │ A │ │ B │ │ C │ │
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│ └────────┘ └────────┘ └────────┘ │
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└──────────────────────────────────────────┘
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```
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---
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## Why This Works
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- **Parallelism** - Multiple agents work simultaneously
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- **Roles** - Specialized agents outperform generalists
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- **Git** - Memory preserved for future agents
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- **Consensus** - Best idea wins, not authority
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---
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## Quick Start
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See [PLAYBOOK.md](PLAYBOOK.md) for detailed instructions.
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```bash
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# Launch a research project
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# 1. Identify your question
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# 2. Decompose into sub-questions
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# 3. Spawn agents using TEMPLATES.md
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# 4. Wait for completion
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# 5. Synthesize results
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```
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---
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## Current Projects
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See [PROJECTS.md](PROJECTS.md) for history.
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---
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*This is a living system. We improve it as we learn.*
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# Research Templates
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## Copy-paste templates for common research tasks
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---
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## Template 1: Spawn Single Researcher
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```
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/spawn agent:main
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label: research-{topic}
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task: Research {question}
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Location: ~/CivONE/
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Research the following question:
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{question}
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Output: docs/papers/research-{topic}.md (3000-4000 words)
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Requirements:
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- Find and cite relevant sources
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- Present multiple perspectives
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- Include experiments if applicable
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```
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---
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## Template 2: Spawn Research Team
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```
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/spawn agent:main
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label: team-{project}
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Task: Research {complex question}
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Location: {location}/
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Each team member takes one aspect:
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**Agent 1 - Deep Research**
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Research: {aspect 1}
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Output: {location}/research-{aspect1}.md
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**Agent 2 - Technical Analysis**
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Research: {aspect 2}
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Output: {location}/research-{aspect2}.md
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**Agent 3 - Synthesis**
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Task: Combine findings into coherent paper
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Output: {location}/papers/{final-paper}.md
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```
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---
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## Template 3: Spawn Experiment
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```
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/spawn agent:main
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label: experiment-{name}
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Task: Build and run experiment
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Location: {location}/
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Build a Python experiment to test: {hypothesis}
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Requirements:
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- Clear hypothesis stated
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- Runnable code
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- Results saved to file
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- Interpretation of results
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Output: experiments/{name}.py + results.txt
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```
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---
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## Template 4: Full Project Launch
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```
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/spawn agent:main
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label: project-{name}
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LAUNCH: {project name}
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QUESTION: {main research question}
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DECOMPOSITION:
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1. {sub-question 1} → Agent: research-{a}
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2. {sub-question 2} → Agent: research-{b}
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3. {sub-question 3} → Agent: research-{c}
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4. {sub-question 4} → Agent: research-{d}
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COORDINATION:
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- All outputs: {location}/papers/
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- Final synthesis: {location}/synthesis.md
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DEADLINE: {time}
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```
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---
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## Git Commands (Manual)
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```bash
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# Push to GitHub
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cd {location}
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git add .
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git commit -m "Research: {topic}"
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git push
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# Pull latest
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git pull origin main
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```
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---
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*Use these templates to launch research quickly*
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Reference in New Issue
Block a user