anysite-lead-generation

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
name: anysite-lead-generation
description: Lead generation and prospecting using anysite MCP server for LinkedIn prospect discovery, email finding, company research, and contact enrichment. Extract contacts from websites, find decision-makers at target companies, and build qualified prospect lists for sales, recruiting, and business development. Supports LinkedIn (primary), web scraping for contact extraction, and Instagram business discovery. Research and list-building only - this skill does NOT write to a CRM. For finding leads AND pushing them into a connected CRM use anysite-crm-prospect; for filling gaps in existing CRM records use anysite-crm-enrich. Use when users need to build prospect lists, find decision-makers, extract contact information, or research potential customers outside a CRM context.
---

# anysite Lead Generation

> Query craft for the two big SQL databases now lives in dedicated skills:
> `anysite-company-sourcing` (70M companies) and `anysite-people-sourcing`
> (856M profiles, `search_sql_users`). Prefer them for bulk list building;
> this skill remains the end-to-end research workflow.

Professional lead generation and prospecting using the anysite MCP server. Find prospects on LinkedIn, discover verified emails, extract contacts from websites, and build comprehensive lead lists for sales, recruiting, and business development.

## Overview

The anysite Lead Generation skill helps you:
- **Find qualified prospects** on LinkedIn using advanced search filters
- **Enrich profiles** with work history, education, and skills
- **Discover email addresses** through LinkedIn email finding
- **Extract contact information** from company websites
- **Research companies** to identify target accounts
- **Build prospect lists** formatted for CRM import

This skill provides comprehensive lead generation capabilities with the added benefit of zero configuration and immediate execution through anysite MCP.

## Supported Platforms

- **LinkedIn** (Primary): People search, profile enrichment, email discovery, company research, employee listings
- **Web Scraping**: Contact extraction from websites, sitemap parsing, general web data
- **Instagram**: Business account discovery and profile analysis (supplementary)
- **Y Combinator**: Startup company and founder research (supplementary)

## v2 API Overview

All data fetching uses the universal `execute()` tool with four parameters: `source`, `category`, `endpoint`, `params`. Supporting tools:

- **`execute(source, category, endpoint, params)`** - Fetch data. Returns first page of items + `cache_key`.
- **`get_page(cache_key, offset, limit)`** - Load additional pages from a previous execute() result using the returned `cache_key` and `next_offset`.
- **`query_cache(cache_key, conditions, sort_by, aggregate, group_by)`** - Filter, sort, count, or aggregate already-fetched data without a new API call.
- **`export_data(cache_key, format)`** - Export a full dataset as CSV, JSON, or JSONL. Returns a download URL.
- **`discover(source, category)`** - Inspect available endpoints and their accepted params (use when unsure about params).

### Error Handling

v2 responses may include an `llm_hint` field with human-readable guidance when something goes wrong (e.g., invalid params, rate limits). Always check for `llm_hint` in the response and follow its advice before retrying.

## Quick Start

### Step 1: Identify Your Lead Source

Choose the appropriate data source based on your prospecting goal:

| Goal | v2 Call | Use Case |
|------|---------|----------|
| Find prospects by title/company | `execute("linkedin", "search", "search_users", {...})` | B2B prospecting, targeted outreach |
| Enrich existing leads | `execute("linkedin", "user", "get", {"user": ...})` | Add work history, education, skills |
| Find verified emails | `execute("linkedin", "email", "find", {"user": ...})` | Email outreach campaigns |
| Extract website contacts | `execute("webparser", "parse", "parse", {"url": ...}