Lead Generation

Purpose

Discover prospects matching an Ideal Customer Profile (ICP), enrich with publicly available data, score on a 0-100 rubric, deduplicate against known leads, and produce structured reports. Pairs naturally with the OSINT skill for deep enrichment on high-scoring leads.

When to Use

• User defines a target market, role, or company profile to prospect.
• A scheduled lead generation cycle triggers.
• An OSINT investigation surfaces a company worth prospecting.
• Strategic reflection identifies a market opportunity to explore.

Pipeline

Phase 1: ICP Construction

Build the Ideal Customer Profile from user requirements:

icp:
  industry: <target industry or industries>
  role: <decision-maker titles (e.g., CTO, VP Engineering, Head of AI)>
  company_size: <startup(1-50) | smb(50-500) | enterprise(500+) | any>
  geography: <region or country focus>
  growth_signals:
    - <what indicates a good prospect (hiring, funding, product launch)>
  tech_stack: <relevant technologies they should use>
  exclusions:
    - <companies or categories to skip>

Phase 2: Discovery Queries

Generate 5-10 search queries combining ICP dimensions:

• "[industry]" "[role]" hiring — active demand signal
• "[industry]" companies "series A" OR "series B" OR "series C" — funded companies
• "top [industry] startups" [year] — curated lists
• site:crunchbase.com "[industry]" "[geography]" — structured data
• "[industry]" "[role]" interview OR podcast — visible decision-makers
• "[industry]" companies "[tech_stack]" — technology fit
• "[industry]" "fastest growing" OR "Inc 5000" OR "emerging" — growth signals

Target: discover 2-3x the desired lead count to allow for filtering.

Phase 3: Enrichment

Three tiers based on configured depth:

Basic (from discovery):

• Person name and title
• Company name
• Source URL

Standard (add web research):

• Company website → employee count, industry, product description
• site:stackshare.io "[company]" OR site:builtwith.com → tech stack
• Job board signals (what roles are they hiring for?)
• Recent news (funding, launches, partnerships)

Deep (add targeted investigation):

• Funding history (Crunchbase, press releases)
• Company news (last 6 months)
• Social profiles (public LinkedIn via site:linkedin.com, Twitter/X)
• Competitive positioning
• Consider triggering OSINT skill for high-value targets

Phase 4: Deduplication

Before scoring, deduplicate against known leads:

Normalization rules:

• Company: strip legal suffixes (Inc, LLC, Ltd, Corp, Co, GmbH, AG, SA), lowercase, remove "The" prefix
• Person: lowercase, remove middle names, handle common nicknames (Bob=Robert, Mike=Michael, Bill=William, Jim=James)

Match criteria (any = duplicate):

• Exact normalized company name + person name
• Fuzzy match (Levenshtein distance < 2)
• Domain match (same company website)

Phase 5: Scoring

Score each lead 0-100 on this rubric:

Category	Max Points	Breakdown
ICP Match	30	Industry match +10, Company size +5, Geography +5, Role/title match +10
Growth Signals	20	Recent funding +8, Actively hiring +6, Product launch +3, Press coverage +3
Enrichment Quality	20	Email pattern found +5, LinkedIn found +5, Full company data +5, Tech stack known +5
Recency	15	Active this month +15, This quarter +10, This year +5
Accessibility	15	Direct contact info +15, Company contact page +10, Social only +5

Score grades:

• A (80-100): Hot lead — high ICP match, strong signals, accessible
• B (60-79): Warm lead — good match, some gaps
• C (40-59): Cool lead — partial match, needs more enrichment
• D (0-39): Cold lead — weak match, archive but don't pursue

Phase 6: Report Generation

# Lead Report: [ICP Description]

**Date:** YYYY-MM-DD
**Leads discovered:** N (after dedup)
**Grade distribution:** A: N, B: N, C: N, D: N

## Hot Leads (A-Grade)

| # | Name | Title | Company | Score | Key Signal |
|---|------|-------|---------|-------|-----------|

## Warm Leads (B-Grade)

| # | Name | Title | Company | Score | Key Signal |
|---|------|-------|---------|-------|-----------|

## Summary
- Total new leads: N
- Duplicates filtered: N
- Top industries represented: ...
- Common growth signals: ...

## Recommended Next Steps
- <which leads to prioritize>
- <what enrichment to run next>
- <ICP refinements based on findings>

Phase 7: State Persistence

• Store leads as observations via MemoryStore
• Tag with ICP profile for future cycle dedup
• Record discovery metadata for report generation

LinkedIn Approach

Compliance-safe pattern:

• Use "[name]" "[company]" site:linkedin.com via search engine
• Only access publicly visible profile information
• Do NOT use LinkedIn API for scraping
• Do NOT bypass login walls
• Do NOT send unsolicited connection requests or messages
• LinkedIn Organization Messaging API (OAuth2) is available for messaging IF the user has proper API credentials and authorization

Email pattern discovery (reference only):

• firstname@domain
• firstname.lastname@domain
• f.lastname@domain
• firstname.l@domain
• Never send unsolicited emails. Pattern discovery is for user reference.

Output Format

report_id: <LEAD-YYYY-MM-DD-NNN>
date: <YYYY-MM-DD>
icp_summary: <one-line ICP description>
leads_discovered: <count>
duplicates_filtered: <count>
leads:
  - name: <person name>
    title: <job title>
    company: <company name>
    score: <0-100>
    grade: <A | B | C | D>
    icp_match:
      industry: <match | partial | no>
      size: <match | partial | no>
      geography: <match | partial | no>
      role: <match | partial | no>
    growth_signals:
      - <signal description>
    enrichment:
      linkedin: <URL or null>
      website: <URL or null>
      tech_stack: [<technologies>]
      recent_funding: <description or null>
    source_urls:
      - <where this lead was found>

References

• src/genesis/skills/osint/SKILL.md — Deep enrichment for high-value leads
• src/genesis/skills/research/SKILL.md — General research methodology
• src/genesis/memory/ — MemoryStore for lead persistence