OpenClaw

lgf

Run B2B lead research with lgf (Lead Gen Factory). Use when asked to find leads, prospect companies, research ICPs, find decision makers, or generate a lead list for any B2B target profile.

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install catafal/lead-gen-factory

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

lgf — Lead Gen Factory

A CLI pipeline that takes a free-text ICP (Ideal Customer Profile) and returns a scored, deduplicated list of B2B leads as both CSV and structured JSON.

Prerequisites

Install lgf once (requires Python 3.12+):

# From the repo root
pip install -e .

# Or via pipx for isolated install
pipx install git+https://github.com/Catafal/lead-gen-factory.git

Verify installation:

lgf doctor

Required API keys (set in ~/.lgf/.env):

TAVILY_API_KEY — web search
OPENROUTER_API_KEY — LLM scoring + extraction

Core Command

lgf research --icp-text "<your ICP>" --json 2>/dev/null

The --json flag outputs a structured JSON envelope to stdout — perfect for AI agents to capture and process without touching the filesystem. All human-facing progress output goes to stderr (suppressed with 2>/dev/null).

Usage Patterns

1. Quick inline ICP (most common)

lgf research --icp-text "HR Directors at SaaS companies in Spain, 50-500 employees" --json 2>/dev/null

2. ICP from file (for complex profiles)

lgf research --icp icp_examples/skillia_spain.md --json 2>/dev/null

3. Narrow with a focus constraint

lgf research --icp-text "Tech companies in Madrid" --focus "only companies hiring L&D managers" --json 2>/dev/null

4. Filter by minimum ICP score

lgf research --icp-text "..." --min-score 8 --json 2>/dev/null

5. Dry-run — see search queries only (no crawling, no LLM calls)

lgf research --icp-text "..." --dry-run

6. Check current config

lgf config

JSON Output Schema

When --json is used, the envelope printed to stdout has this structure:

{
  "leads": [
    {
      "business": "Acme Corp",
      "first": "Ana",
      "last": "García",
      "email": "ana.garcia@acme.com",
      "linkedin": "https://linkedin.com/in/anagarcia",
      "website": "https://acme.com",
      "phone": null,...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/catafal/lead-gen-factory
Version: —
Commit: 8f74ddc41759189791ed325b46b35ce0b16f6ca1
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# lgf — Lead Gen Factory

A CLI pipeline that takes a free-text ICP (Ideal Customer Profile) and returns
a scored, deduplicated list of B2B leads as both CSV and structured JSON.

## Prerequisites

Install lgf once (requires Python 3.12+):

```bash
# From the repo root
pip install -e .

# Or via pipx for isolated install
pipx install git+https://github.com/Catafal/lead-gen-factory.git
```

Verify installation:

```bash
lgf doctor
```

Required API keys (set in `~/.lgf/.env`):
- `TAVILY_API_KEY` — web search
- `OPENROUTER_API_KEY` — LLM scoring + extraction

---

## Core Command

```bash
lgf research --icp-text "<your ICP>" --json 2>/dev/null
```

The `--json` flag outputs a structured JSON envelope to stdout — perfect for
AI agents to capture and process without touching the filesystem.
All human-facing progress output goes to stderr (suppressed with `2>/dev/null`).

---

## Usage Patterns

### 1. Quick inline ICP (most common)

```bash
lgf research --icp-text "HR Directors at SaaS companies in Spain, 50-500 employees" --json 2>/dev/null
```

### 2. ICP from file (for complex profiles)

```bash
lgf research --icp icp_examples/skillia_spain.md --json 2>/dev/null
```

### 3. Narrow with a focus constraint

```bash
lgf research --icp-text "Tech companies in Madrid" --focus "only companies hiring L&D managers" --json 2>/dev/null
```

### 4. Filter by minimum ICP score

```bash
lgf research --icp-text "..." --min-score 8 --json 2>/dev/null
```

### 5. Dry-run — see search queries only (no crawling, no LLM calls)

```bash
lgf research --icp-text "..." --dry-run
```

### 6. Check current config

```bash
lgf config
```

---

## JSON Output Schema

When `--json` is used, the envelope printed to stdout has this structure:

```json
{
  "leads": [
    {
      "business": "Acme Corp",
      "first": "Ana",
      "last": "García",
      "email": "ana.garcia@acme.com",
      "linkedin": "https://linkedin.com/in/anagarcia",
      "website": "https://acme.com",
      "phone": null,...

搜索

lgf