# lead-enricher > Enriches inbound leads with firmographic and technographic data. Takes minimal lead info and enriches via WebSearch, Apollo, or ZoomInfo to produce a complete lead record with company size, revenue, tech stack, social profiles, and decision-maker details. - Skill: `ekatasingh1107/lead-enricher` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add ekatasingh1107/lead-enricher` - Raw SKILL.md: https://api.skillmd.com/api/skills/ekatasingh1107/lead-enricher/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Finance & Business - Author: ekatasingh1107 (https://skillmd.com/u/ekatasingh1107) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/ekatasingh1107/lead-enricher --- # Lead Enricher Takes raw inbound lead data (often just a name and email) and enriches it into a complete lead record. Uses WebSearch for public data, checks `tools.lead_enrichment` config for Apollo or ZoomInfo API paths, and outputs a fully populated lead record ready for qualification and routing. ## Prerequisites - `agency.config.json` populated (ICP, tools config) - WebSearch tool available - Inbound lead data: at minimum, email address or company name - Optional: Apollo.io API access (configured in `tools.lead_enrichment`) - Optional: ZoomInfo access (configured in `tools.lead_enrichment`) ## Capabilities Used 1. `company-researcher` -- deep company data extraction 2. `person-researcher` -- contact-level enrichment 3. `linkedin-researcher` -- LinkedIn profile data 4. `email-validator` -- verify email deliverability 5. `crm-writer` -- write enriched record back to CRM ## Phase 0: Intake Read `agency.config.json`: - `icp.segments[]` -- for ICP fit tagging during enrichment - `tools.lead_enrichment` -- check which enrichment tools are available: ```json { "tools": { "lead_enrichment": { "primary": "apollo", "apollo_api_key": "env:APOLLO_API_KEY", "zerobounce_api_key": "env:ZEROBOUNCE_API_KEY", "fallback": "websearch" } } } ``` - `crm.tabs` -- where to write enriched records - `services[]` -- for service-need matching during enrichment Accept parameters: - `lead` -- (required) lead object with available fields - `mode` -- `single` | `batch`. Default: `single` - `leads` -- (required if mode = `batch`) array of lead objects - `enrichment_depth` -- `basic` | `standard` | `deep`. Default: `standard` - `validate_email` -- boolean, run email validation. Default: `true` - `write_to_crm` -- boolean, write enriched record to CRM. Default: `true` Minimum lead input: ```json { "email": "priya@freshskin.co" } ``` Or: ```json { "contact_name": "Priya Mehta", "company_name": "FreshSkin Co" } ``` ## Phase 1: Email Domain Extraction If only email provided: - Extract domain: `freshskin.co` - Derive company domain: check if `freshskin.co` resolves to a website - Flag freemail domains (gmail.com, yahoo.com, outlook.com) -- lower quality lead signal If company name provided but no domain: - WebSearch: `"{{company_name}}" website` - Extract the primary domain ## Phase 2: Contact Enrichment ### Apollo Path (if tools.lead_enrichment.primary = "apollo") Use Apollo People Match or People Search: - Input: email or name + company - Extract: full name, title, phone, LinkedIn URL, company details - Apollo returns: person match confidence, verified email, company firmographics ### WebSearch Path (if Apollo unavailable or as supplement) Run `person-researcher` with available data: **Name + Company search**: - WebSearch: `"{{contact_name}}" "{{company_name}}"` - WebSearch: `"{{contact_name}}" linkedin` - WebSearch: `"{{contact_name}}" {{company_domain}}` **Extract**: - Full name (verify spelling) - Current title and role - LinkedIn profile URL - Twitter/X handle - Other social profiles - Professional background summary - Recent activity (posts, interviews, conference appearances) ### LinkedIn Enrichment Run `linkedin-researcher` for: - Profile headline and summary - Current role details (start date, description) - Previous roles (career trajectory) - Education - Skills and endorsements - Connections count (proxy for network size) - Recent posts (content they care about) - Groups (interests and focus areas) Compile: ``` CONTACT RECORD --- Full name: [verified name] Email: [email] Email status: [valid/invalid/catch-all/unknown] Title: [current title] Phone: [if found] LinkedIn: [URL] Twitter: [handle] Location: [city, country] Seniority: [C-level/VP/Director/Manager/Individual] Department: [Marketing/Ecommerce/Engineering/Operations/Executive] ``` ## Phase 3: Company Enrichment ### Apollo Path (if available) Use Apollo Organization Enrich: - Input: domain - Extract: company name, industry, employee count, revenue range, tech stack, social profiles ### WebSearch Path Run `company-researcher` with the company domain: **Firmographic data**: - Company legal name - Industry and sub-industry (SIC/NAICS if available) - Employee count (exact or range) - Revenue estimate (range) - Founded year - Headquarters location - Funding status (bootstrapped, seed, Series A/B/C, public) - Total funding raised **Digital presence**: - Website URL and platform (Shopify, WooCommerce, custom, etc.) - Technology stack (via BuiltWith signals in search results) - Social profiles: LinkedIn company page, Instagram, Twitter, Facebook, YouTube - App store presence (if applicable) - Review sites (G2, Capterra, Trustpilot, Google Reviews) **Business signals**: - Recent funding rounds - Job postings (roles, departments expanding) - Press mentions (last 6 months) - Partnership announcements - Product launches - Awards or recognition - Competitor landscape (who else is in their space) Compile: ``` COMPANY RECORD --- Company name: [legal name] Domain: [primary domain] Industry: [vertical] Sub-industry: [niche] Employee count: [range] Revenue estimate: [range] Founded: [year] HQ: [city, country] Funding: [status + total raised] Platform: [ecommerce platform] Tech stack: [key technologies detected] Social profiles: LinkedIn: [URL] Instagram: [handle] Twitter: [handle] Facebook: [URL] YouTube: [URL] Recent signals: Hiring: [roles if any] Funding: [recent round if any] Press: [notable mentions] Product: [recent launches] ``` ## Phase 4: Email Validation If `validate_email` = true: ### ZeroBounce Path (if configured) - Submit email to ZeroBounce API - Get: status (valid/invalid/catch-all/spamtrap/abuse/unknown), sub-status, domain info ### Heuristic Path (if no API) - Check MX records for domain - Check if domain is active - Flag disposable email domains - Flag role-based emails (info@, hello@, support@) Validation result: ``` Email validation: Address: [email] Status: [valid/invalid/risky/unknown] Type: [personal/role-based/freemail] Deliverability: [HIGH/MEDIUM/LOW] Risk flags: [catch-all, new domain, etc.] ``` ## Phase 5: ICP Tagging Match enriched data against `icp.segments[]`: For each segment, check: - Industry match - Company size match (employee range) - Revenue range match - Geography match - Platform match - Stage match (post-PMF, mid-market, enterprise) Tag the lead: ``` ICP Assessment: Best segment match: [segment name] Fit score: [1-5] Matching criteria: [list of matches] Non-matching criteria: [list of misses] Fit verdict: STRONG_FIT / MODERATE_FIT / WEAK_FIT / NO_FIT ``` ## Phase 6: Service Need Detection Based on enriched data, flag potential service needs: - **Store development**: Outdated store, non-Shopify platform, poor mobile experience - **CRO**: High traffic + low conversion signals, basic product pages - **Catalog management**: Large SKU count, poor product imagery - **Performance marketing**: Low traffic, no paid ads visible, competitor ads running - **SEO**: Low organic visibility, thin content, no blog - **Email marketing**: No email capture visible, no post-purchase flow ``` Detected needs: 1. [Service] -- [signal that indicates this need] -- Confidence: [HIGH/MEDIUM/LOW] 2. [Service] -- [signal] -- Confidence: [level] ``` ## Phase 7: Output Return structured JSON: ```json { "enrichment_date": "2026-03-07", "enrichment_depth": "standard", "contact": { "full_name": "Priya Mehta", "email": "priya@freshskin.co", "email_valid": true, "email_type": "personal", "title": "Founder & CEO", "phone": "+91-98XXXXXXXX", "linkedin": "https://linkedin.com/in/priyamehta", "twitter": "@priyamehta", "location": "Mumbai, India", "seniority": "C-level", "department": "Executive" }, "company": { "name": "FreshSkin Co", "domain": "freshskin.co", "industry": "Beauty & Skincare", "sub_industry": "D2C Skincare", "employee_count": "25-50", "revenue_estimate": "INR 5-10 Cr/year", "founded": 2022, "hq": "Mumbai, India", "funding": {"status": "Series A", "total_raised": "INR 8 Cr"}, "platform": "Shopify", "tech_stack": ["Shopify", "Klaviyo", "Google Analytics"], "social": { "linkedin": "https://linkedin.com/company/freshskinco", "instagram": "@freshskinco", "twitter": "@freshskinco" }, "recent_signals": { "hiring": ["Marketing Manager", "Content Creator"], "funding": "Series A closed Dec 2025", "product_launches": ["New serum line launched Jan 2026"] } }, "icp_assessment": { "best_segment": "Post-PMF D2C India", "fit_score": 4, "fit_verdict": "STRONG_FIT", "matching": ["industry", "size", "geo", "platform", "stage"], "non_matching": [] }, "detected_needs": [ {"service": "CRO", "signal": "Basic product pages, no trust elements", "confidence": "HIGH"}, {"service": "Catalog Management", "signal": "50+ SKUs with inconsistent photography", "confidence": "MEDIUM"} ], "enrichment_sources": ["websearch", "linkedin", "zerobounce"], "data_completeness": "85%", "missing_fields": ["phone", "revenue_exact"], "generated_at": "2026-03-07T10:00:00Z" } ``` ## Phase 8: CRM Write and Handoff If `write_to_crm` = true: - Write enriched record to CRM via `crm-writer` - Set `enrichment_status` = "complete" - Set `enrichment_date` = today - Set `icp_fit` = fit verdict Handoff options: - If `auto_qualify` flag set, trigger `lead-qualifier` with enriched data - If `auto_route` flag set, trigger `lead-router` after qualification - Otherwise, present enriched record for manual review **APPROVAL GATE**: "Lead enriched. [Data completeness]% complete. Route to qualification?" ## Example Usage Trigger phrases: - "Enrich this lead: priya@freshskin.co" - "I got a new inbound lead, enrich them: [name] at [company]" - "Batch enrich this week's inbound leads" - "Look up everything about [person] at [company]" - "Fill in the missing data for [lead]" - "Enrich and qualify [lead name]"