anysite-icp-builder

What this skill does

# AnySite ICP Builder

Build data-driven Ideal Customer Profiles from LinkedIn data using AnySite MCP tools.

## Overview

This skill transforms manual ICP creation into an automated, data-driven workflow:
1. **Analyze** existing customers' LinkedIn profiles
2. **Extract** common patterns (industries, company sizes, titles, skills)
3. **Generate** scoring criteria and ICP documentation
4. **Find** lookalike prospects matching the ICP

## When to Use This Skill

- Building or refining an Ideal Customer Profile
- Analyzing LinkedIn profiles of existing customers
- Finding common patterns among best customers
- Creating prospect scoring criteria
- Discovering lookalike companies/people
- Researching target market segments

## Prerequisites

- AnySite MCP server connected with LinkedIn access
- List of existing customer LinkedIn URLs (profiles or companies)

---

## Phase 1: ICP Discovery Interview

Ask the user these questions to gather context:

### Required Questions (ask first)
1. **Customer URLs**: "Please provide LinkedIn URLs of 5-15 of your best existing customers (profiles or company pages)"
2. **Product/Service**: "What problem does your product solve? Who benefits most?"

### Optional Questions (ask if not provided)
3. **Deal Size**: "What's your typical deal size or ACV?"
4. **Sales Cycle**: "How long is your typical sales cycle?"
5. **Geographic Focus**: "Any geographic restrictions (US, EU, APAC, etc.)?"
6. **Exclusions**: "Any industries or company types to exclude?"

---

## Phase 2: Data Collection

Use AnySite MCP tools to gather data:

### For LinkedIn Profile URLs (individuals)
```
Tools to use:
- Anysite:get_linkedin_profile - Get full profile with experience, education, skills
- Anysite:get_linkedin_user_posts - Analyze recent activity and interests
- Anysite:get_linkedin_user_experience - Detailed work history
```

### For LinkedIn Company URLs
```
Tools to use:
- Anysite:get_linkedin_company - Company details, size, industry
- Anysite:get_linkedin_company_employees - Key employees and roles
- Anysite:get_linkedin_company_employee_stats - Employee distribution data
- Anysite:get_linkedin_company_posts - Company activity and messaging
```

### Data Points to Extract

**From Profiles:**
- Current title and seniority level
- Company name and industry
- Years of experience (total and current role)
- Skills and endorsements
- Education background
- Location
- Recent post topics and engagement

**From Companies:**
- Industry classification
- Employee count range
- Headquarters location
- Company description/tagline
- Specialties/focus areas
- Recent hiring patterns
- Content themes from posts

---

## Phase 3: Pattern Analysis

After collecting data, analyze for patterns:

### Demographic Patterns
```markdown
## Company Demographics
- **Industries**: [List top 3-5 industries with percentages]
- **Company Size**: [Employee range, e.g., "50-500 employees (70%)"]
- **Stage**: [Startup/Growth/Enterprise distribution]
- **Geography**: [Primary regions]
- **Tech Stack Indicators**: [Common technologies mentioned]

## Decision Maker Demographics  
- **Titles**: [Top 5 titles with frequency]
- **Seniority**: [C-level/VP/Director/Manager distribution]
- **Functions**: [Engineering/Sales/Marketing/Product etc.]
- **Tenure**: [Average years in role and at company]
```

### Behavioral Patterns
```markdown
## Engagement Signals
- **Content Interests**: [Topics they post/engage with]
- **Activity Level**: [Posting frequency]
- **Network Size**: [Connection ranges]
- **Group Memberships**: [Common groups/communities]
```

### Firmographic Patterns
```markdown
## Company Characteristics
- **Growth Indicators**: [Hiring, funding, expansion signals]
- **Technology Adoption**: [Tools/platforms mentioned]
- **Business Model**: [B2B/B2C/Marketplace etc.]
- **Maturity Level**: [Years in business, funding stage]
```

---

## Phase 4: ICP Document Generation

Generate a comprehensive ICP document with this structure:

```markdown
# Ideal Customer Profile: [Company Name]

## Executive Summary
[2-3 sentence overview of ideal customer]

## Company Profile

### Must-Have Criteria (Hard Requirements)
| Criterion | Requirement | Weight |
|-----------|-------------|--------|
| Industry | [Specific industries] | 25% |
| Company Size | [Employee range] | 20% |
| Geography | [Regions] | 15% |
| [Custom] | [Requirement] | X% |

### Nice-to-Have Criteria (Soft Requirements)
| Criterion | Preference | Weight |
|-----------|------------|--------|
| Tech Stack | [Technologies] | 10% |
| Growth Stage | [Funding/stage] | 10% |
| [Custom] | [Preference] | X% |

## Decision Maker Profile

### Primary Buyer Persona
- **Title**: [Most common title]
- **Seniority**: [Level]
- **Function**: [Department]
- **Responsibilities**: [Key duties]
- **Pain Points**: [Problems they face]
- **Success Metrics**: [What they're measured on]

### Secondary Influencers
[List other roles involved in buying decision]

## Scoring Model

### Prospect Scoring (100 points max)

**Company Fit (50 points)**
- Industry exact match: 20 pts
- Industry adjacent: 10 pts
- Company size in range: 15 pts
- Geographic match: 10 pts
- Tech stack match: 5 pts

**Contact Fit (30 points)**
- Title exact match: 15 pts
- Title similar: 8 pts
- Seniority match: 10 pts
- Function match: 5 pts

**Engagement Signals (20 points)**
- Recent relevant activity: 10 pts
- Content engagement: 5 pts
- Network overlap: 5 pts

### Score Interpretation
- **80-100**: Hot prospect - prioritize outreach
- **60-79**: Warm prospect - add to nurture
- **40-59**: Cool prospect - monitor for signals
- **Below 40**: Low priority - deprioritize

## Anti-ICP (Exclusion Criteria)
- [Industry/type to avoid]
- [Company characteristics that don't fit]
- [Red flags to watch for]

## Validated Against
- [X] customers analyzed
- Analysis date: [Date]
- Data source: LinkedIn via AnySite MCP
```

---

## Phase 5: Prospect Discovery

Use the ICP to find lookalike prospects:

### Search Strategies

**By Company Attributes:**
```
Tool: Anysite:search_linkedin_companies
Parameters:
- industry: [From ICP]
- employee_count: [Size range]
- location: [Geography]
- keywords: [Industry terms]
```

**By Decision Maker Profile:**
```
Tool: Anysite:search_linkedin_users
Parameters:
- title: [Target title]
- company_keywords: [Industry/type]
- location: [Geography]
- keywords: [Relevant terms]
```

**By Company Employees:**
```
Tool: Anysite:get_linkedin_company_employees
Parameters:
- companies: [Target company URNs]
- keywords: [Title keywords]
```

### Prospect Enrichment

For each discovered prospect:
1. Fetch full profile/company data
2. Apply scoring model
3. Calculate fit score
4. Identify personalization hooks

---

## Output Formats

### ICP Summary Report
Save as: `icp-report-[company]-[date].md`

### Prospect List
Save as: `prospects-[company]-[date].json`

```json
{
  "icp_version": "1.0",
  "generated_date": "YYYY-MM-DD",
  "prospects": [
    {
      "name": "Company/Person Name",
      "linkedin_url": "URL",
      "score": 85,
      "score_breakdown": {
        "company_fit": 45,
        "contact_fit": 25,
        "engagement": 15
      },
      "match_reasons": ["Industry match", "Title match"],
      "personalization_hooks": ["Recent post about X", "Hiring for Y"]
    }
  ]
}
```

---

## Example Workflow

```
User: "Help me build an ICP based on my best customers"

Claude:
1. Ask for customer LinkedIn URLs
2. Collect data using AnySite MCP tools
3. Analyze patterns across all profiles
4. Generate ICP document with scoring model
5. Optionally: Search for lookalike prospects
6. Output: ICP report + prospect list
```

---

## Best Practices

1. **Minimum Sample Size**: Analyze at least 5 customers for reliable patterns
2. **Mix of Data**: Include both "best" customers and "average" ones for contrast
3. **Regular Updates**: Refresh ICP quarterly as customer base evolves
4. **Validate with Sales**: Cross-check patterns with sales team knowledge
5. **Iter