apollo-reference-architecture
Deploy a layered reference architecture for robust Apollo.io integrations.
Install
mkdir -p .claude/skills/apollo-reference-architecture && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7126" && unzip -o skill.zip -d .claude/skills/apollo-reference-architecture && rm skill.zipInstalls to .claude/skills/apollo-reference-architecture
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implement Apollo.io reference architecture.Key capabilities
- →Design layered API service architectures
- →Implement background job processing with BullMQ
- →Manage deal pipeline and opportunity stages
- →Sync data using service layer patterns
- →Establish production-grade authentication
How it works
It organizes code into distinct layers including an API client, service layer, background job queues, and a database model. This structure separates business logic from API communication and handles asynchronous tasks.
Inputs & outputs
When to use apollo-reference-architecture
- →Design scalable Apollo API service layers
- →Integrate background job queues with Apollo data
- →Sync Apollo data with CRM systems
- →Establish production-grade authentication patterns
About this skill
Apollo Reference Architecture
Overview
Production-ready reference architecture for Apollo.io integrations. Layered design with API client, service layer, background jobs, database models, CRM sync, and deals pipeline — all built around Apollo's REST API with correct endpoints and x-api-key authentication.
Prerequisites
- Apollo master API key
- Node.js 18+ with TypeScript
- PostgreSQL for data layer
- Redis for job queues
Instructions
Step 1: Architecture Diagram
┌───────────────────────────────────────────────┐
│ API Layer │ Express routes
│ POST /api/leads/search GET /api/org/:d │ POST /api/deals
├───────────────────────────────────────────────┤
│ Service Layer │ Business logic
│ LeadService EnrichService DealService │ SequenceService
├───────────────────────────────────────────────┤
│ Client Layer │ Apollo API wrapper
│ ApolloClient RateLimiter Cache │ CreditTracker
├───────────────────────────────────────────────┤
│ Background Jobs │ BullMQ queues
│ EnrichJob SyncJob StageChangeJob │ TaskCreatorJob
├───────────────────────────────────────────────┤
│ Data Layer │ Prisma/TypeORM
│ Contact Organization Deal AuditLog │
└───────────────────────────────────────────────┘
Step 2: Service Layer
// src/services/lead-service.ts
import { getApolloClient } from '../apollo/client';
import { withRetry } from '../apollo/retry';
import { cachedRequest } from '../apollo/cache';
export class LeadService {
private client = getApolloClient();
async searchPeople(params: { domains: string[]; titles?: string[]; seniorities?: string[]; page?: number }) {
return cachedRequest('/mixed_people/api_search',
() => withRetry(() => this.client.post('/mixed_people/api_search', {
q_organization_domains_list: params.domains,
person_titles: params.titles,
person_seniorities: params.seniorities,
page: params.page ?? 1, per_page: 100,
})),
params,
);
}
async enrichPerson(email: string) {
return withRetry(() => this.client.post('/people/match', { email }));
}
async enrichOrg(domain: string) {
return cachedRequest('/organizations/enrich',
() => withRetry(() => this.client.get('/organizations/enrich', { params: { domain } })),
{ domain },
);
}
}
Step 3: Deals/Opportunities Service
Apollo has a full Deals API for tracking revenue pipeline.
// src/services/deal-service.ts
export class DealService {
private client = getApolloClient();
async createDeal(params: {
name: string;
amount: number;
ownerId: string; // Apollo user ID
accountId?: string; // Apollo account ID
contactIds?: string[]; // Apollo contact IDs
stageId?: string; // Deal stage ID
}) {
const { data } = await this.client.post('/opportunities', {
name: params.name,
amount: params.amount,
owner_id: params.ownerId,
account_id: params.accountId,
contact_ids: params.contactIds,
opportunity_stage_id: params.stageId,
});
return { dealId: data.opportunity.id, name: data.opportunity.name };
}
async listDeals(page: number = 1) {
const { data } = await this.client.post('/opportunities/search', { page, per_page: 50 });
return data.opportunities.map((d: any) => ({
id: d.id, name: d.name, amount: d.amount,
stage: d.opportunity_stage?.name, owner: d.owner?.name,
}));
}
async getDealStages() {
const { data } = await this.client.get('/opportunity_stages');
return data.opportunity_stages.map((s: any) => ({ id: s.id, name: s.name, order: s.display_order }));
}
async updateDeal(dealId: string, updates: { amount?: number; stageId?: string }) {
await this.client.patch(`/opportunities/${dealId}`, {
amount: updates.amount,
opportunity_stage_id: updates.stageId,
});
}
}
Step 4: Background Job Processing
// src/jobs/enrichment-job.ts
import { Queue, Worker, Job } from 'bullmq';
import { LeadService } from '../services/lead-service';
const connection = { host: process.env.REDIS_HOST ?? 'localhost', port: 6379 };
export const enrichmentQueue = new Queue('apollo-enrichment', {
connection,
defaultJobOptions: {
attempts: 3,
backoff: { type: 'exponential', delay: 5000 },
removeOnComplete: 1000,
},
});
const leadService = new LeadService();
new Worker('apollo-enrichment', async (job: Job) => {
switch (job.name) {
case 'enrich-person':
return leadService.enrichPerson(job.data.email);
case 'enrich-org':
return leadService.enrichOrg(job.data.domain);
case 'bulk-search': {
const results: any[] = [];
for (const domain of job.data.domains) {
const { data } = await leadService.searchPeople({ domains: [domain] });
results.push(...data.people);
await job.updateProgress(results.length);
}
return { total: results.length };
}
}
}, { connection, concurrency: 3, limiter: { max: 50, duration: 60_000 } });
Step 5: Database Model
// src/models/contact.ts (Prisma schema excerpt)
// model Contact {
// id String @id @default(cuid())
// apolloId String @unique
// emailHash String @unique // keyed/blind index; not the email itself
// contactCiphertext Bytes // application-encrypted, minimized contact fields
// organizationId String?
// enrichedAt DateTime?
// createdAt DateTime @default(now())
// updatedAt DateTime @updatedAt
// }
// TypeORM version
import { Entity, Column, PrimaryColumn, CreateDateColumn, UpdateDateColumn } from 'typeorm';
@Entity('contacts')
export class Contact {
@PrimaryColumn() apolloId: string;
@Column({ unique: true }) emailHash: string; // keyed hash for deduplication
@Column({ type: 'bytea' }) contactCiphertext: Buffer; // app-encrypted fields only
@Column({ nullable: true }) organizationId: string;
@Column({ nullable: true }) enrichedAt: Date;
@CreateDateColumn() createdAt: Date;
@UpdateDateColumn() updatedAt: Date;
}
Step 6: API Routes
// src/api/routes.ts
import { Router } from 'express';
import { LeadService } from '../services/lead-service';
import { DealService } from '../services/deal-service';
const router = Router();
const leads = new LeadService();
const deals = new DealService();
router.post('/api/leads/search', async (req, res) => {
const { data } = await leads.searchPeople(req.body);
res.json({ leads: data.people, pagination: data.pagination });
});
router.post('/api/leads/enrich', async (req, res) => {
const { data } = await leads.enrichPerson(req.body.email);
res.json({ contact: data.person });
});
router.get('/api/organizations/:domain', async (req, res) => {
const { data } = await leads.enrichOrg(req.params.domain);
res.json({ organization: data.organization });
});
router.post('/api/deals', async (req, res) => {
const result = await deals.createDeal(req.body);
res.json(result);
});
router.get('/api/deals', async (req, res) => {
const list = await deals.listDeals(parseInt(req.query.page as string) || 1);
res.json({ deals: list });
});
export { router };
Output
- Layered architecture: API, Service, Client, Jobs, Data
LeadServicewith cached search and retried enrichmentDealServicewith create, list, update, and stage management- BullMQ background jobs for async enrichment
- Database model (Prisma + TypeORM)
- Express API routes for search, enrichment, and deals
Examples
For an initial architecture rollout, begin with free search behind an authenticated service boundary, retain only the organization and lead fields the approved use case needs, and encrypt contact data before it enters the database. Store a keyed hash only for deduplication; never persist raw Apollo responses or expose enrichment routes directly without authorization, budget, and audit controls. Run a small staging queue with an explicit dead-letter review path and validate that retrying a job cannot create duplicate contacts or deals. If encryption, authorization, budget enforcement, or idempotency is not demonstrably active, stop the rollout and keep the workflow in the non-production environment.
Error Handling
| Layer | Strategy |
|---|---|
| Client | Retry with backoff, circuit breaker for prolonged outages |
| Service | Cache fallback on failure, credit budget enforcement |
| Jobs | 3 retries with exponential backoff, dead letter after max |
| API | Structured JSON error responses with error codes |
Resources
Next Steps
Proceed to apollo-multi-env-setup for environment configuration.
When not to use it
- →For simple, single-script integrations
- →When Redis is not available for job queues
Prerequisites
Limitations
- →Requires specific infrastructure like Redis and PostgreSQL
How it compares
This provides a structured, production-ready architecture rather than a flat script-based integration.
Compared to similar skills
apollo-reference-architecture side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| apollo-reference-architecture (this skill) | 1 | 2mo | Caution | Advanced |
| moai-domain-backend | 1 | 4mo | Review | Advanced |
| agentdb-advanced-features | 7 | 11mo | Review | Advanced |
| agent-dev-backend-api | 3 | 7mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
moai-domain-backend
modu-ai
Backend development specialist covering API design, database integration, microservices architecture, and modern backend patterns.
agentdb-advanced-features
ruvnet
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
agent-dev-backend-api
ruvnet
Agent skill for dev-backend-api - invoke with $agent-dev-backend-api
caching-strategies
dadbodgeoff
Implement multi-layer caching with Redis, in-memory, and HTTP caching. Covers cache invalidation, stampede prevention, and cache-aside patterns.
add-module
fullstackhero
Create a new module (bounded context) with proper project structure, permissions, DbContext, and registration. Use when adding a new business domain that needs its own entities and endpoints.
pagination
dadbodgeoff
Implement cursor-based and offset pagination for APIs. Covers efficient database queries, stable sorting, and pagination metadata.