| name | lead-hand-skill |
| version | 1.0.0 |
| description | Expert knowledge for AI lead generation — web research, enrichment, scoring, deduplication, and report generation |
| runtime | prompt_only |
Lead Generation Expert Knowledge
Ideal Customer Profile (ICP) Construction
A good ICP answers these questions:
- Industry: What vertical does your ideal customer operate in?
- Company size: How many employees? What revenue range?
- Geography: Where are they located?
- Technology: What tech stack do they use?
- Budget signals: Are they funded? Growing? Hiring?
- Decision-maker: Who has buying authority? (title, seniority)
- Pain points: What problems does your product solve for them?
Company Size Categories
| Category | Employees | Typical Budget | Sales Cycle |
|---|
| Startup | 1-50 | $1K-$25K/yr | 1-4 weeks |
| SMB | 50-500 | $25K-$250K/yr | 1-3 months |
| Enterprise | 500+ | $250K+/yr | 3-12 months |
Web Research Techniques for Lead Discovery
Search Query Patterns
# Find companies in a vertical
"[industry] companies" site:crunchbase.com
"top [industry] startups [year]"
"[industry] companies [city/region]"
# Find decision-makers
"[title]" "[company]" site:linkedin.com
"[company] team" OR "[company] about us" OR "[company] leadership"
# Growth signals (high-intent leads)
"[company] hiring [role]" — indicates budget and growth
"[company] series [A/B/C]" — recently funded
"[company] expansion" OR "[company] new office"
"[company] product launch [year]"
# Technology signals
"[company] uses [technology]" OR "[company] built with [technology]"
site:stackshare.io "[company]"
site:builtwith.com "[company]"
Source Quality Ranking
- Company website (About/Team pages) — most reliable for personnel
- Crunchbase — funding, company details, leadership
- LinkedIn (public profiles) — titles, tenure, connections
- Press releases — announcements, partnerships, funding
- Job boards — hiring signals, tech stack requirements
- Industry directories — comprehensive company lists
- News articles — recent activity, reputation
- Social media — engagement, company culture
Lead Enrichment Patterns
Basic Enrichment (always available)
- Full name (first + last)
- Job title
- Company name
- Company website URL
Standard Enrichment
- Company employee count (from About page, Crunchbase, or LinkedIn)
- Company industry classification
- Company founding year
- Technology stack (from job postings, StackShare, BuiltWith)
- Social profiles (LinkedIn URL, Twitter handle)
- Company description (from meta tags or About page)
Deep Enrichment
- Recent funding rounds (amount, investors, date)
- Recent news mentions (last 90 days)
- Key competitors
- Estimated revenue range
- Recent job postings (growth signals)
- Company blog/content activity (engagement level)
- Executive team changes
Email Pattern Discovery
Common corporate email formats (try in order):
firstname@company.com (most common for small companies)firstname.lastname@company.com (most common for larger companies)first_initial+lastname@company.com (e.g., jsmith@)firstname+last_initial@company.com (e.g., johns@)
Note: NEVER send unsolicited emails. Email patterns are for reference only.
Lead Scoring Framework
Scoring Rubric (0-100)
ICP Match (30 points max):
Industry match: +10
Company size match: +5
Geography match: +5
Role/title match: +10
Growth Signals (20 points max):
Recent funding: +8
Actively hiring: +6
Product launch: +3
Press coverage: +3
Enrichment Quality (20 points max):
Email found: +5
LinkedIn found: +5
Full company data: +5
Tech stack known: +5
Recency (15 points max):
Active this month: +15
Active this quarter:+10
Active this year: +5
No recent activity: +0
Accessibility (15 points max):
Direct contact: +15
Company contact: +10
Social only: +5
No contact info: +0
Score Interpretation
| Score | Grade | Action |
|---|
| 80-100 | A | Hot lead — prioritize outreach |
| 60-79 | B | Warm lead — nurture |
| 40-59 | C | Cool lead — enrich further |
| 0-39 | D | Cold lead — deprioritize |
Deduplication Strategies
Matching Algorithm
- Exact match: Normalize company name (lowercase, strip Inc/LLC/Ltd) + person name
- Fuzzy match: Levenshtein distance < 2 on company name + same person
- Domain match: Same company website domain = same company
- Cross-source merge: Same person at same company from different sources → merge enrichment data
Normalization Rules
Company name:
- Strip legal suffixes: Inc, LLC, Ltd, Corp, Co, GmbH, AG, SA
- Lowercase
- Remove "The" prefix
- Collapse whitespace
Person name:
- Lowercase
- Remove middle names/initials
- Handle "Bob" = "Robert", "Mike" = "Michael" (common nicknames)
Output Format Templates
CSV Format
Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
"Jane Smith","VP Engineering","Acme Corp","https://acme.com","https://linkedin.com/in/janesmith","SaaS","SMB (120 employees)",85,"2025-01-15","Series B funded, hiring 5 engineers"
JSON Format
[
{
"name": "Jane Smith",
"title": "VP Engineering",
"company": "Acme Corp",
"company_url": "https://acme.com",
"linkedin": "https://linkedin.com/in/janesmith",
"industry": "SaaS",
"company_size": "SMB",
"employee_count": 120,
"score": 85,
"discovered": "2025-01-15",
"enrichment": {
"funding": "Series B, $15M",
"hiring": true,
"tech_stack": ["React", "Python", "AWS"],
"recent_news": "Launched enterprise plan Q4 2024"
},
"notes": "Strong ICP match, actively growing"
}
]
Markdown Table Format
| # | Name | Title | Company | Score | Key Signal |
| --- | --- | --- | --- | --- | --- |
| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | Series B funded, hiring |
| 2 | John Doe | CTO | Beta Inc | 72 | Product launch Q1 2025 |
Compliance & Ethics
DO
- Use only publicly available information
- Respect robots.txt and rate limits
- Include data provenance (where each piece of info came from)
- Allow users to export and delete their lead data
- Clearly mark confidence levels on enriched data
DO NOT
- Scrape behind login walls or paywalls
- Fabricate any lead data (even "likely" email addresses without evidence)
- Store sensitive personal data (SSN, financial info, health data)
- Send unsolicited communications on behalf of the user
- Bypass anti-scraping measures (CAPTCHAs, rate limits)
- Collect data on individuals who have opted out of data collection
Data Retention
- Keep lead data in local files only — never exfiltrate
- Mark stale leads (>90 days without activity) for review
- Provide clear data export in all supported formats
