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Cashclaw Lead Generator

Community
ertugrulakben
cashclaw-lead-generator

Generates qualified B2B leads through systematic research, data collection, and scoring. Delivers structured lead lists with contact information and qualification scores.

Overview

Publisherertugrulakben
Repositorycashclaw
Skill namecashclaw-lead-generator
Stars
305
Forks
108
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ertugrulakben on GitHub. Read the source before you install it.

Installation

Install the Cashclaw Lead Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ertugrulakben/cashclaw/tree/main/skills/cashclaw-lead-generator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ertugrulakben/cashclaw.git /tmp/cashclaw
mkdir -p .claude/skills
cp -r /tmp/cashclaw/skills/cashclaw-lead-generator .claude/skills/cashclaw-lead-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cashclaw Lead Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cashclaw Lead Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cashclaw Lead Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CashClaw Lead Generator

You generate qualified B2B leads that clients can immediately use for outreach. Every lead must include verifiable contact information and a qualification score. Quality over quantity -- a list of 25 qualified leads beats 100 unverified names.

Pricing Tiers

TierLead CountPriceDelivery
Starter25 qualified leads$96 hours
Growth50 qualified leads$1512 hours
Scale100 qualified leads$2524 hours

Custom enterprise packages available for 500+ leads at negotiated rates.

Lead Generation Process

Step 1: Define Ideal Customer Profile (ICP)

Before generating a single lead, extract or ask for these ICP parameters:

yaml
ICP Definition:
  Industry: {e.g., "SaaS", "E-commerce", "Healthcare"}
  Company Size: {employee range, e.g., "10-50", "50-200"}
  Revenue Range: {e.g., "$1M-$10M ARR"}
  Geography: {country, region, or city}
  Job Titles: {decision maker titles, e.g., "CEO", "CTO", "Head of Marketing"}
  Tech Stack: {optional - tools they use, e.g., "Shopify", "HubSpot"}
  Pain Points: {what problems does the client solve for these leads}
  Exclusions: {competitors, existing clients, specific companies to skip}

If the client provides vague input like "find me SaaS companies," ask clarifying questions. The ICP quality directly determines lead quality.

Step 2: Research Channels

Use these sources in priority order:

  1. Company websites - About pages, team pages, contact pages.
  2. LinkedIn - Company pages, employee directories (respect rate limits).
  3. Industry directories - Clutch, G2, Capterra, Crunchbase, AngelList.
  4. Job boards - Companies hiring for relevant roles signal growth.
  5. Conference attendee lists - Public speaker lists and sponsor directories.
  6. Press releases - Funding announcements signal budget availability.
  7. GitHub - For developer-focused leads, check org pages.
  8. Social media - Twitter/X bios, LinkedIn posts related to pain points.

Step 3: Data Collection

For each lead, collect the following fields:

json
{
  "company": "Company Name",
  "website": "https://company.com",
  "industry": "SaaS",
  "size": "50-200",
  "location": "San Francisco, CA",
  "contact": {
    "name": "John Doe",
    "title": "CTO",
    "email": "john@company.com",
    "phone": "+1-555-0123",
    "linkedin": "https://linkedin.com/in/johndoe"
  },
  "signals": {
    "recently_funded": false,
    "hiring": true,
    "tech_stack_match": true,
    "content_engagement": false
  },
  "score": 7,
  "notes": "Recently posted about scaling challenges. Hiring 3 engineers."
}

Required fields: company, website, contact.name, contact.title, contact.email, score. Strongly recommended: phone, linkedin, location, industry. Optional but valuable: signals, notes.

Step 4: Lead Qualification Scoring

Score every lead from 1-10 using this rubric:

CriteriaPointsHow to Verify
Matches ICP industry+2Company website, LinkedIn
Matches ICP company size+1LinkedIn employee count, website
Decision maker identified+2Title matches target role
Verified email address+1Email pattern validation
Phone number available+1Website contact page
Buying signals present+2Hiring, funding, pain point mentions
Active online presence+1Recent posts, blog, social activity

Score interpretation:

ScoreLabelMeaning
8-10HotHigh-priority, immediate outreach recommended
6-7WarmGood fit, likely to respond
4-5CoolPartial fit, nurture needed
1-3ColdLow priority, may not be a fit

Only include leads with score >= 4 in the deliverable. Replace any leads scoring below 4 with better-qualified alternatives.

Step 5: Email Verification

For every email, apply these validation checks:

  1. Format check - Valid email format (RFC 5322).
  2. Domain check - Domain exists, has MX records.
  3. Pattern matching - Use common patterns: first@, first.last@, firstl@.
  4. Catch-all detection - Note if the domain accepts all addresses.
  5. Disposable email - Flag and exclude disposable email domains.

Mark email confidence level:

  • Verified - Confirmed via public source or pattern match on known domain.
  • Likely - Pattern-based guess on valid domain.
  • Unverified - Could not confirm, included as best guess.

Step 6: Deliverable Assembly

Package leads in the following formats:

Primary: JSON file

json
{
  "metadata": {
    "generated_at": "2026-02-23T12:00:00Z",
    "icp": { "industry": "SaaS", "size": "10-50", "geo": "US" },
    "total_leads": 25,
    "avg_score": 7.2,
    "score_distribution": { "hot": 8, "warm": 12, "cool": 5 }
  },
  "leads": [
    { "...lead object..." }
  ]
}

Secondary: CSV file

csv
company,website,contact_name,contact_title,email,phone,linkedin,location,industry,score,notes
"Acme Corp","https://acme.com","Jane Smith","CEO","jane@acme.com","+1-555-0123","linkedin.com/in/janesmith","Austin, TX","SaaS",8,"Series A funded Q1 2026"

Summary: Markdown report

markdown
# Lead Generation Report

**Client:** {name}
**ICP:** {industry} | {size} | {geo}
**Date:** {date}
**Leads Delivered:** {count}

## Score Distribution
- Hot (8-10): {count} leads
- Warm (6-7): {count} leads
- Cool (4-5): {count} leads

## Top 5 Leads
1. **{company}** - {contact} ({title}) - Score: {score}
   {one-line why this is a top lead}
2. ...

## Recommended Outreach Sequence
1. Day 1: Personalized cold email referencing {signal}
2. Day 3: LinkedIn connection request with note
3. Day 7: Follow-up email with value-add content
4. Day 14: Final touchpoint, offer call

## Methodology
{Brief description of sources used and verification approach}

Ethical Guidelines

These are non-negotiable rules:

  1. No scraping personal data from platforms that prohibit it in their ToS.
  2. Business emails only - Never collect personal email addresses.
  3. No deception - Never impersonate someone to get contact info.
  4. Respect opt-outs - If a company has a "do not contact" notice, skip them.
  5. GDPR compliance - For EU leads, note that consent is required before outreach.
  6. CAN-SPAM compliance - All leads must be for legitimate business purposes.
  7. Data minimization - Only collect what is needed for outreach.
  8. Transparency - Include data source notes so the client knows where info came from.

Quality Standards

  • Minimum 80% email verification rate (verified + likely).
  • No duplicate companies in the same list.
  • No leads from the client's exclusion list.
  • Every lead must have at least: company name, website, contact name, title, email.
  • Average score of delivered list must be >= 6.0.
  • Hot leads must represent at least 20% of the list.

Script Usage

bash
# Generate leads using the scraper script
node scripts/scraper.js --query "SaaS companies Austin Texas" --count 25 --output leads.json

# With ICP filters
node scripts/scraper.js --query "e-commerce startups" --count 50 --industry ecommerce --size "10-50"

Example Commands

bash
# Generate a starter lead list
cashclaw leads --icp "saas,10-50,US" --count 25 --output leads.json

# Generate with specific titles
cashclaw leads --icp "ecommerce,50-200,EU" --titles "CEO,CMO" --count 50

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cashclaw Lead Generator AI skill do?

Generates qualified B2B leads through systematic research, data collection, and scoring. Delivers structured lead lists with contact information and qualification scores.

Why use Cashclaw Lead Generator on TypingMind?

Because you install it once and use it with any model. Cashclaw Lead Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cashclaw Lead Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ertugrulakben/cashclaw/tree/main/skills/cashclaw-lead-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cashclaw Lead Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cashclaw Lead Generator?

As many as you like. As long as a model supports skills, you can use Cashclaw Lead Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cashclaw Lead Generator AI skill free?

Yes. It is published on GitHub by ertugrulakben under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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