A comprehensive data enrichment suite that provides AI agents with 25 specialized tools to source and verify professional person and company intelligence.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install enrich-layer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install enrich-layer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Enrich Layer?
Enrich Layer is a high-performance skill designed for the Openclaw Skills ecosystem, enabling seamless integration with the Enrich Layer API. It provides a robust interface for retrieving structured data on companies, individuals, job postings, and educational institutions directly from professional networks. By leveraging this skill, developers can automate complex data gathering tasks such as finding decision-makers, verifying work emails, and mapping out company hierarchies with precision.
The skill is built on a credit-based system that optimizes for cost-efficiency, offering various lookup strategies ranging from free metadata checks to deep-profile enrichment. It is an essential tool for sales intelligence, recruitment automation, and market research workflows within any Openclaw Skills powered environment.
Enrich Layer Use Cases
- Automating lead generation by finding decision-makers and their verified work emails.
- Conducting deep market research by searching for companies based on industry, size, and funding status.
- Enriching CRM data with up-to-date professional profiles and contact information.
- Verifying lead quality using disposable email detection and reverse email lookups.
- Recruiting talent by searching for specific alumni or monitoring job postings and employee counts.
How Enrich Layer Works
- The AI agent receives a query related to a person, company, or contact.
- The agent checks current credit availability using the meta tool to ensure operation viability.
- A search or lookup tool is triggered (e.g., company_lookup) to retrieve a unique professional network identifier.
- The agent uses the retrieved identifier to pull detailed profile data or specific contact details like phone numbers and emails.
- Data is processed and returned in a structured format, often utilizing the inline enrichment shortcut to minimize API calls.
- Cache settings are applied based on user requirements for either speed or data freshness.
Enrich Layer Setup
To integrate Enrich Layer into your environment, add the server to your configuration and provide your API key. Ensure you have Node.js installed.
# Install the Enrich Layer MCP server
npx -y @verticalint-michael/enrich-layer-mcp
Configure your environment variable:
export ENRICH_LAYER_API_KEY='your_api_key_here'
Add to your Openclaw Skills configuration:
{
"mcpServers": {
"enrich-layer": {
"command": "npx",
"args": ["-y", "@verticalint-michael/enrich-layer-mcp"],
"env": {
"ENRICH_LAYER_API_KEY": "${ENRICH_LAYER_API_KEY}"
}
}
}
}
Enrich Layer Data Schema & Taxonomy
Enrich Layer organizes data into five primary entities, accessible via specific tools within the Openclaw Skills framework:
| Entity | Key Data Points |
|---|---|
| Company | Name, Industry, Size, Funding, Acquisitions, Employee Count |
| Person | Experience, Education, Skills, Professional Network URL |
| Contact | Work Email, Personal Email, Personal Phone (E.164), Disposable Status |
| Job | Posting Details, Experience Level, Flexibility, Location |
| School | Student Lists, Alumni Data, Major/Field of Study |
Enrich Layer Advanced Features
- Boolean Search Logic: Supports complex queries using AND, OR, NOT, and exact phrase matching for targeted searches.
- Profile Inlining: Use the enrich_profile parameter to combine lookup and enrichment into a single API transaction.
- Intelligent Caching: Control data freshness with 'if-present' or 'if-recent' (under 29 days) cache behaviors.
- Bulk Processing Support: Efficiently handles employee and student lists with configurable page sizes.
- Global Filtering: Precision targeting using ISO 3166-1 alpha-2 country codes across all search tools.
SKILL.md
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