lead-hand-skill — Поиск и квалификация лидов с помощью AI

--- 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: 1. **Industry**: What vertical does your ideal customer operate in? 2. **Company size**: How many employees? What revenue range? 3. **Geography**: Where are they located? 4. **Technology**: What tech stack do they use? 5. **Budget signals**: Are they funded? Growing? Hiring? 6. **Decision-maker**: Who has buying authority? (title, seniority) 7. **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 1. **Company website** (About/Team pages) — most reliable for personnel 2. **Crunchbase** — funding, company details, leadership 3. **LinkedIn** (public profiles) — titles, tenure, connections 4. **Press releases** — announcements, partnerships, funding 5. **Job boards** — hiring signals, tech stack requirements 6. **Industry directories** — comprehensive company lists 7. **News articles** — recent activity, reputation 8. **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): 1. `firstname@company.com` (most common for small companies) 2. `firstname.lastname@company.com` (most common for larger companies) 3. `first_initial+lastname@company.com` (e.g., jsmith@) 4. `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 1. **Exact match**: Normalize company name (lowercase, strip Inc/LLC/Ltd) + person name 2. **Fuzzy match**: Levenshtein distance < 2 on company name + same person 3. **Domain match**: Same company website domain = same company 4. **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 ```csv 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 ```json [ { "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 ```markdown | # | 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