Claude Code GTM Prospect Research

An intelligent B2B prospect research command for Claude Code that automates customer discovery through AI-powered searches and generates comprehensive prospect reports.

Overview

Transform manual prospect research into an automated workflow where you define your Ideal Customer Profile (ICP) once, and Claude finds, researches, and prioritizes potential customers using the Exa search engine.

Key Features:

• 🎯 ICP-Driven Research: Simple 5-question setup defines your ideal customer
• 🤖 AI-Powered Discovery: Automated search query generation and execution
• 📊 Smart Scoring: 100-point algorithm prioritizes prospects by fit
• 💰 Cost Optimized: Configurable API usage with bulk data retrieval
• 📁 Campaign Management: Organized outputs for tracking multiple campaigns
• 🔄 Multi-Format Output: Human-readable reports + machine-readable JSON

Quick Start

Prerequisites

• Claude Code installed
• Exa MCP Server configured
• Exa API key

Installation

1. Clone this repository:

   git clone https://github.com/your-username/claude-code-gtm-prospects.git
   cd claude-code-gtm-prospects

2. Copy commands to your project:

   # For project-specific usage
   cp -r commands/ /your/project/.claude/commands/
   
   # For global usage across all projects
   cp -r commands/ ~/.claude/commands/

3. Configure Exa MCP Server: Add to your Claude configuration:

   {
     "mcpServers": {
       "exa": {
         "command": "npx",
         "args": ["-y", "@modelcontextprotocol/server-exa"],
         "env": {
           "EXA_API_KEY": "your-exa-api-key-here"
         }
       }
     }
   }

Usage

# Basic usage (3 API calls, 10 results each)
/gtm-prospect

# Custom API budget (5 calls, 15 results each)  
/gtm-prospect 5 15

# Maximum research (10 calls, 20 results each)
/gtm-prospect 10 20

How It Works

Phase 1: ICP Definition

Answer 5 strategic questions to define your target customer:

1. Industry/Vertical: What industry are you targeting?
2. Company Size: What employee count range?
3. Geography: What geographic regions?
4. Value Proposition: What problem do you solve?
5. Decision Makers: Who are the typical buyers?

Phase 2: Automated Research

Claude executes intelligent searches using:

• Company Research: Industry + size + geography queries
• Signal Discovery: Decision maker + technology adoption searches
• Decision Maker Mapping: LinkedIn profile discovery

Phase 3: Intelligent Scoring

100-point algorithm evaluates prospects on:

• Industry exact match (40 points)
• Company size fit (30 points)
• Geographic location (15 points)
• Recent activity signals (10 points)
• Decision maker visibility (5 points)

Phase 4: Report Generation

Outputs comprehensive reports with:

• Prioritized prospect lists (High/Medium/Low)
• Company intelligence and growth signals
• Personalized outreach strategies
• Performance analytics and cost tracking

Examples

B2B SaaS Example

# SaaS Security Tools - Prospect Research

**API Configuration**: 3 calls, 10 results each

## Research Summary
- **API Budget Used**: 3/3 calls (28 total results)
- **High Priority**: 4 prospects (80+ score)
- **Medium Priority**: 8 prospects (60-79 score)
- **Cost Efficiency**: 9.3 prospects per API call

## Top Tier Prospects (80+ Score)

### 1. Datadog - Score: 87/100
**Industry**: SaaS | **Employees**: 3,000 | **Location**: New York, NY
**Why Top Tier**: Perfect industry match, recent security acquisitions

**Outreach Strategy**: Reference their recent security platform expansion and offer API monitoring integration

See examples/ for complete sample outputs.

Repository Structure

claude-code-gtm-prospects/
├── commands/                     # Claude command definitions  
│   └── gtm-prospect.md          # Main prospect research command
├── examples/                    # Example outputs and campaigns
│   └── b2b-saas-campaign/      # Sample SaaS campaign
├── gtm-prospects/              # Generated campaign data
│   └── templates/              # Template files
├── README.md                   # This file
└── LICENSE                     # MIT License

Command

Command	Description
/gtm-prospect [calls] [results]	Automated B2B prospect research workflow

Configuration Options

API Budget Control

# Conservative (3 calls × 10 = 30 prospects)
/gtm-prospect 3 10

# Balanced (5 calls × 15 = 75 prospects)  
/gtm-prospect 5 15

# Comprehensive (8 calls × 20 = 160 prospects)
/gtm-prospect 8 20

Cost Estimation

• Basic research: ~$0.03-0.05 per campaign
• Enhanced research: ~$0.08-0.12 per campaign
• Comprehensive research: ~$0.15-0.25 per campaign

Best Practices

1. Start Specific: Narrow ICPs yield higher quality prospects
2. Budget Wisely: Use 3-5 calls for initial research, scale up if promising
3. Track Campaigns: Use timestamp-based folders for comparison
4. Export Data: Leverage JSON outputs for CRM integration
5. Regular Updates: Weekly runs capture market changes

Troubleshooting

Command not found:

• Check file exists in .claude/commands/
• Verify Claude Code can access commands directory

Exa API errors:

• Validate API key configuration
• Check MCP server is running: claude mcp list
• Ensure sufficient API credits

Poor results:

• Broaden industry terms or geographic scope
• Increase results-per-call parameter
• Try different value proposition keywords

Contributing

We welcome contributions! Ideas for enhancement:

• Industry-specific templates
• CRM integration commands
• Prospect tracking and follow-up
• Multi-language support
• Advanced scoring algorithms

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Test changes with your Exa API key
4. Commit changes: git commit -m 'Add amazing feature'
5. Push to branch: git push origin feature/amazing-feature
6. Open a Pull Request

License

MIT License - see LICENSE file for details.

Acknowledgments

• Inspired by claude-code-requirements-builder
• Built for the Claude Code ecosystem
• Powered by Exa Search intelligence