Optimizes provider selection, routing, and credit usage across 150+ sources for email discovery, company intelligence, and contact enrichment. Useful for waterfall sequences, performance audits, and GTM/RevOps workflows.
From data-enrichment-masternpx claudepluginhub gtmagents/gtm-agents --plugin data-enrichment-masterThis skill uses the workspace's default tool permissions.
references/provider_cheat_sheet.mdscripts/cost_calculator.pyGuides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Enforces A/B test setup with gates for hypothesis locking, metrics definition, sample size calculation, assumptions checks, and execution readiness before implementation.
Designs, audits, and improves analytics tracking systems using Signal Quality Index for reliable, decision-ready data in marketing, product, and growth.
You are an expert at selecting and optimizing data providers from 150+ available options to maximize data quality while minimizing credit costs. Use this layered framework to keep enrichment predictable and efficient.
Best Input Scenarios:
Quality Tiers:
Data Type Priority:
Industry Specialization:
Tier 0 (Free): Native operations, cached data, manual inputs
Tier 1 (0.5 credits): Validation, verification, basic lookups
Tier 2 (1-2 credits): Standard enrichments (Apollo, Hunter, Clearbit)
Tier 3 (2-3 credits): Premium data (ZoomInfo, technographics, intent)
Tier 4 (3-5 credits): Enterprise intelligence (PitchBook, custom AI)
Tier 5 (5-10 credits): Specialized services (video generation, deep AI research)
1. Cache Everything
2. Batch Processing
# Process in batches for volume discounts
if record_count > 1000:
use_provider("apollo_bulk") # 10-30% discount
elif record_count > 100:
use_parallel_processing()
else:
use_standard_processing()
3. Smart Waterfalls
waterfall_sequence = [
{"provider": "cache", "credits": 0},
{"provider": "apollo", "credits": 1.5, "stop_if_success": True},
{"provider": "hunter", "credits": 1.2, "stop_if_success": True},
{"provider": "bettercontact", "credits": 3, "stop_if_success": True},
{"provider": "ai_research", "credits": 5, "last_resort": True}
]
Priority: Success rate over cost
Sequence:
1. BetterContact (aggregates 10+ sources)
2. ZoomInfo (if enterprise)
3. Apollo + Hunter + RocketReach
4. AI web research
Expected Success: 95%+
Average Cost: 8-12 credits
Priority: Good success with reasonable cost
Sequence:
1. Apollo.io
2. Hunter (if domain match)
3. RocketReach (if name match)
4. Stop or continue based on confidence
Expected Success: 80%
Average Cost: 3-5 credits
Priority: Minimize cost
Sequence:
1. Cache check
2. Hunter (domain only)
3. Free sources (Google, LinkedIn public)
4. Stop at first result
Expected Success: 60%
Average Cost: 1-2 credits
def calculate_data_quality_score(data, sources):
score = 0
# Multi-source validation (30 points)
if len(sources) > 1:
score += min(len(sources) * 10, 30)
# Data completeness (30 points)
required_fields = ["email", "phone", "title", "company"]
score += sum(10 for field in required_fields if data.get(field))
# Verification status (20 points)
if data.get("email_verified"):
score += 10
if data.get("phone_verified"):
score += 10
# Recency (20 points)
days_old = get_data_age(data)
if days_old < 30:
score += 20
elif days_old < 90:
score += 10
return score
# Combine AI and traditional providers
def hybrid_enrichment(company):
# Fast, cheap base data
base = clearbit_lookup(company)
# AI for missing pieces
if not base.get("description"):
base["description"] = ai_generate_description(company)
# Premium for high-value
if is_enterprise_account(base):
base.update(zoominfo_enrich(company))
return base
# Enrich in stages based on engagement
def progressive_enrichment(lead):
# Stage 1: Basic (on import)
if lead.stage == "new":
return basic_enrichment(lead) # 1-2 credits
# Stage 2: Engaged (opened email)
elif lead.stage == "engaged":
return standard_enrichment(lead) # 3-5 credits
# Stage 3: Qualified (booked meeting)
elif lead.stage == "qualified":
return comprehensive_enrichment(lead) # 10+ credits
references/provider_cheat_sheet.md for provider selection.scripts/cost_calculator.py for estimating credit usage.// JavaScript/Node.js template
const enrichContact = async (name, company) => {
// Check cache first
const cached = await checkCache(name, company);
if (cached) return cached;
// Try providers in sequence
const providers = ['apollo', 'hunter', 'rocketreach'];
for (const provider of providers) {
try {
const result = await callProvider(provider, {name, company});
if (result.email) {
await saveToCache(result);
return result;
}
} catch (error) {
console.log(`${provider} failed, trying next...`);
}
}
// Fallback to AI research
return await aiResearch(name, company);
};
Progressive disclosure: Load full provider details and code examples only when actively optimizing enrichment workflows