hubspot-integration

id: SKL-hubspot-HUBSPOTINTEGRATION name: Hubspot Integration description: HubSpot is an inbound marketing and sales platform with comprehensive APIs. This guide covers CRM objects, workflows, webhooks, and integration patterns for syncing data and automating marketing and s version: 1.0.0 status: active owner: '@cerebra-team' last_updated: '2026-02-22' category: Backend tags:

• api
• backend
• server
• database stack:
• Python
• Node.js
• REST API
• GraphQL difficulty: Intermediate

Hubspot Integration

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

HubSpot is an inbound marketing and sales platform with comprehensive APIs. This guide covers CRM objects, workflows, webhooks, and integration patterns for syncing data and automating marketing and sales processes.

Why This Matters

• Marketing Automation: HubSpot provides powerful marketing tools
• Sales Pipeline: Built-in deal management and tracking
• Integration: Well-documented APIs for easy integration
• Scalability: HubSpot handles large volumes

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • HubSpot API key or OAuth token
  • Contact data
  • Deal information
  • Webhook URLs
• Entry Conditions:
  • HubSpot account configured
  • API credentials available
  • Webhook endpoints set up
• Outputs:
  • Synced CRM objects
  • Webhook event processing
  • Workflow triggers
  • Marketing campaign results
• Artifacts Required (Deliverables):
  • HubSpot client service
  • Webhook handler
  • Sync service
  • Workflow automation
• Acceptance Evidence:
  • Unit tests for API integration
  • Integration tests with HubSpot
  • E2E tests for webhook processing
• Success Criteria:
  • API calls < 500ms
  • Webhook processing < 1 second
  • Sync completes within 5 minutes

Skill Composition

• Depends on: Contact Management, Lead Management
• Compatible with: Sales Pipeline, Salesforce Integration
• Conflicts with: None
• Related Skills: API Design, Webhook Handling, Marketing Integration

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure