UitbreidenOS Lead Enrichment Skill
When to activate
- Building a pipeline that turns a raw email/URL list into rich prospect profiles
- Scraping company websites and LinkedIn for firmographic data
- Populating HubSpot/Salesforce records from external research
- Generating ICP scores based on enriched company data
- Monitoring signals (funding rounds, hiring surges, exec changes) for account triggers
When NOT to use
- Single ad-hoc lookups — use a browser or LinkedIn directly
- Bulk B2C consumer data — different regulations and data sources
- When verified data is already in your CRM — don't re-enrich unnecessarily
Instructions
The enrichment pipeline
Raw Input (email / domain / LinkedIn URL)
↓
Step 1: IDENTIFY — resolve email to person + company
Step 2: ENRICH — fetch firmographic data (company, tech stack, headcount)
Step 3: SIGNAL CHECK — recent news, funding, hiring, exec changes
Step 4: SCORE — ICP fit score
Step 5: STORE — upsert to CRM with enriched fields
Email → person resolution
// Using Hunter.io API
async function resolveEmailToPerson(email: string): Promise<PersonData | null> {
const res = await fetch(
`https://api.hunter.io/v2/email-verifier?email=${email}&api_key=${process.env.HUNTER_API_KEY}`
)
const data = await res.json()
if (data.data.status !== 'valid') return null
return {
email,
firstName: data.data.first_name,
lastName: data.data.last_name,
company: data.data.organization,
domain: getDomain(email),
}
}
// Using Apollo.io (richer data)
async function enrichFromApollo(email: string) {
const res = await fetch('https://api.apollo.io/v1/people/match', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'x-api-key': process.env.APOLLO_API_KEY! },
body: JSON.stringify({ email }),
})
return res.json()
}
Company enrichment via web scraping (Firecrawl)
import FirecrawlApp from '@mendable/firecrawl-js'
const firecrawl = new FirecrawlApp({ apiKey: process.env.FIRECRAWL_API_KEY })
async function enrichCompanyFromWebsite(domain: string): Promise<CompanyData> {
// Scrape the company website with structured extraction
const result = await firecrawl.scrapeUrl(`https://${domain}`, {
formats: ['extract'],
extract: {
schema: {
type: 'object',
properties: {
companyName: { type: 'string' },
description: { type: 'string' },
industry: { type: 'string' },
products: { type: 'array', items: { type: 'string' } },
techStack: { type: 'array', items: { type: 'string' } },
teamSize: { type: 'string' },
founded: { type: 'number' },
headquarters: { type: 'string' },
},
},
},
})
return result.extract as CompanyData
}
Signal detection (trigger-based outreach)
async function detectTriggerSignals(company: string): Promise<TriggerSignal[]> {
const signals: TriggerSignal[] = []
// Funding signals — check TechCrunch, Crunchbase
const fundingNews = await searchRecentNews(`${company} funding round 2026`)
if (fundingNews.length > 0) {
signals.push({ type: 'funding', relevance: 0.9, context: fundingNews[0].title })
}
// Hiring signals — engineering hires suggest growth/new projects
const hiringData = await checkLinkedInJobs(company)
if (hiringData.engineeringJobCount > 10) {
signals.push({ type: 'hiring_surge', relevance: 0.7, context: `${hiringData.engineeringJobCount} open engineering roles` })
}
// Tech stack changes — new tools = new needs
const techChanges = await checkBuiltWithHistory(getDomain(company))
if (techChanges.recentAdditions.length > 0) {
signals.push({ type: 'tech_adoption', relevance: 0.6, context: `Added: ${techChanges.recentAdditions.join(', ')}` })
}
return signals.sort((a, b) => b.relevance - a.relevance)
}
Claude-powered profile synthesis
async function synthesiseProspectProfile(
person: PersonData,
company: CompanyData,
signals: TriggerSignal[]
): Promise<ProspectProfile> {
const { object } = await generateObject({
model: anthropic('claude-opus-4-7'),
schema: z.object({
icpScore: z.number().min(0).max(100),
painPoints: z.array(z.string()),
outreachAngle: z.string(),
bestChannel: z.enum(['email', 'linkedin', 'cold_call']),
bestTiming: z.string(),
notAGoodFit: z.boolean(),
notAGoodFitReason: z.string().optional(),
}),
prompt: `Analyse this prospect for a ${process.env.OUR_PRODUCT_DESCRIPTION}.
Person: ${person.firstName} ${person.lastName}, ${person.jobTitle} at ${company.companyName}
Company: ${company.description}. ${company.teamSize} employees. ${company.industry}.
Tech stack: ${company.techStack.join(', ')}
Recent signals: ${signals.map(s => s.context).join('; ')}
Score their ICP fit, identify pain points we can solve, and suggest the best outreach angle.`,
})
return { ...person, ...company, signals, ...object }
}
Full pipeline
async function enrichLeadList(emails: string[]): Promise<EnrichedLead[]> {
const results: EnrichedLead[] = []
for (const email of emails) {
try {
console.log(`Enriching ${email}...`)
const [person, company] = await Promise.all([
resolveEmailToPerson(email),
enrichCompanyFromWebsite(getDomain(email)),
])
if (!person || !company) {
results.push({ email, status: 'not_found' })
continue
}
const signals = await detectTriggerSignals(company.companyName)
const profile = await synthesiseProspectProfile(person, company, signals)
// Upsert to HubSpot
await upsertHubSpotContact(email, profile)
results.push({ email, status: 'enriched', profile })
// Rate limiting
await new Promise(r => setTimeout(r, 500))
} catch (err) {
results.push({ email, status: 'error', error: err.message })
}
}
return results
}
Example
User: Build a pipeline that takes 50 company domains from a CSV, scrapes each website for company data, detects funding and hiring signals, scores ICP fit, and pushes results to HubSpot.
Expected output:
scripts/enrich-pipeline.ts— reads domains.csv, runs enrichment, writes results.jsonenrichCompanyFromWebsite(domain)— Firecrawl structured extractiondetectTriggerSignals(company)— funding + hiring + tech signalsscoreICP(company, criteria)— 0-100 scoreupsertHubSpotContact(email, enrichedData)— creates/updates CRM records- Progress logging, error capture to
failed-enrichments.csv