lead-generation

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Prospect discovery, enrichment, scoring, and reporting against an Ideal Customer Profile

AI & Automation 89 stars 16 forks Updated today MIT

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Skill Content

# 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: ```yaml 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 pod...

Details

Author
WingedGuardian
Repository
WingedGuardian/GENesis-AGI
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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